Analyze AFM tip solvation¶

This notebook demonstrates deposition of an SDS adsorption layer on a non-spherical AFM tip model.

Initialization¶

IPython magic¶

%load_ext autoreload
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

%aimport

Modules to reload:
all-except-skipped

Modules to skip:

Imports¶

import ase.io # here used for reading pdb files
from ase.visualize import view
from ase.visualize.plot import plot_atoms # has nasty offset issues
from cycler import cycler # here used for cycling through colors in plots
import datetime
import fabric # for pythonic ssh connections
from fireworks import LaunchPad, Firework, Tracker, Workflow 
from fireworks import FileTransferTask, PyTask, ScriptTask

# FireWorks functionality 
from fireworks import Firework, LaunchPad, ScriptTask, Workflow
from fireworks.user_objects.firetasks.templatewriter_task import TemplateWriterTask
from fireworks.user_objects.firetasks.filepad_tasks import AddFilesTask, GetFilesTask, GetFilesByQueryTask
from imteksimfw.fireworks.user_objects.firetasks.cmd_tasks import CmdTask
from fireworks.utilities.filepad import FilePad # direct FilePad access, similar to the familiar LaunchPad

from collections.abc import Iterable
import glob
import gc # manually clean up memory with gc.collect()
import gromacs # GromacsWrapper, here used for evoking gmc commands, reading and writing .ndx files
# from io import StringIO, TextIOWrapper
import io
from IPython.display import display, Image #, Video # display image files within notebook
from ipywidgets import Video  # display video within notebook
import itertools # for products of iterables
import json # generic serialization of lists and dicts
import jinja2 # here used for filling packmol input script template
import jinja2.meta # for gathering variables in a jinja2 template
import logging 
import matplotlib.pyplot as plt
import MDAnalysis as mda # here used for reading and analyzing gromacs trajectories
import MDAnalysis.analysis.rdf as mda_rdf
import MDAnalysis.analysis.rms as mda_rms
from mpl_toolkits.mplot3d import Axes3D # here used for 3d point cloud scatter plot
import miniball # finds minimum bounding sphere of a point set
import nglview
import numpy as np
import os, os.path
import pandas as pd
import panedr # reads GROMACS edr into pandas df, requires pandas and pbr
import parmed as pmd # has quite a few advantages over ASE when it comes to parsing pdb
from pprint import pprint
import pymongo # for sorting in queries
import scipy.constants as sc
import subprocess # used for evoking external packmol
import sys
import tempfile
import yaml

GromacsWrapper might need a file ~/.gromacswrapper.cfg with content

[Gromacs]
tools = gmx gmx_d 
# gmx_mpi_d gmx_mpi_d

# name of the logfile that is written to the current directory
logfilename = gromacs.log

# loglevels (see Python's logging module for details)
#   ERROR   only fatal errors
#   WARN    only warnings
#   INFO    interesting messages
#   DEBUG   everything

# console messages written to screen
loglevel_console = INFO

# file messages written to logfilename
loglevel_file = DEBUG

in order to know the GROMACS executables it is allowed to use. Otherwise, calls to gmx_mpi or gmx_mpi_d without MPI wrapper might lead to MPI warnings in output that cause GromacsWrapper to fail.

Logging¶

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
logger.setLevel(logging.INFO)

ParmEd needs to know the GROMACS topology folder, usually get this from envionment variable GMXLIB:

Function definitions¶

def find_undeclared_variables(infile):
    """identify all variables evaluated in a jinja 2 template file"""
    env = jinja2.Environment()
    with open(infile) as template_file:
        parsed = env.parse(template_file.read())

    undefined = jinja2.meta.find_undeclared_variables(parsed)
    return undefined

def memuse():
    """Quick overview on memory usage of objects in Jupyter notebook"""
    # https://stackoverflow.com/questions/40993626/list-memory-usage-in-ipython-and-jupyter
    # These are the usual ipython objects, including this one you are creating
    ipython_vars = ['In', 'Out', 'exit', 'quit', 'get_ipython', 'ipython_vars']

    # Get a sorted list of the objects and their sizes
    return sorted([(x, sys.getsizeof(globals().get(x))) for x in dir(sys.modules['__main__']) if not x.startswith('_') and x not in sys.modules and x not in ipython_vars], key=lambda x: x[1], reverse=True)

Global settings¶

# pandas settings
pd.options.display.max_rows = 200
pd.options.display.max_columns = 16
pd.options.display.max_colwidth = 256

os.environ['GMXLIB'] = '/gmx_top'

# pmd.gromacs.GROMACS_TOPDIR = os.environ['GMXLIB']
pmd.gromacs.GROMACS_TOPDIR = '/gmx_top'

# prefix = '/mnt/dat/work/testuser/indenter/sandbox/20191110_packmol'
prefix = '/mnt/dat/work'

work_prefix = '/mnt/dat/work/tmp'

try:
    os.mkdir(work_prefix)
except FileExistsError as exc:
    print(exc)

[Errno 17] File exists: '/mnt/dat/work/tmp'

os.chdir(work_prefix)

# the FireWorks LaunchPad
lp = LaunchPad.auto_load() #Define the server and database
# FilePad behaves analogous to LaunchPad
fp = FilePad.auto_load()

Conversion from LAMMPS data format to PDB¶

The following bash / tcl snippet converts a LAMMPS data file to PDB, assigning the desired names as mapped in a yaml file

#!/bin/bash
# echo "package require jlhvmd; jlh lmp2pdb indenter.lammps indenter.pdb" | vmd -eofexit
vmd -eofexit << 'EOF'
package require jlhvmd
topo readlammpsdata indenter.lammps
jlh type2name SDS_type2name.yaml
jlh name2res  SDS_name2res.yaml
set sel [atomselect top all]
$sel writepdb indenter.pdb
EOF

pdb_chain.py indenter.pdb > indenter_wo_chainid.pdb
pdb_reres_by_atom_9999.py indenter_wo_chainid.pdb > indenter_reres.pdb

Requires

VMD (tested with 1.9.3) with topotools
jlhvmd VMD plugin: https://github.com/jotelha/jlhvmd
pdb-tools: https://github.com/haddocking/pdb-tools/

Overview¶

Overview on projects in database¶

query = {'metadata.datetime': {'$gt': '2020'} }

fp.filepad.count_documents(query)

8937

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 'project': '$metadata.project' },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # pull 'project' field up in hierarchy
        "$addFields": { 
            "project": "$_id.project",
        },
    },
    {  # drop nested '_id.project'
        "$project": { 
            "_id": False 
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

# sort_aggregation
#aggregation_pipeline = [ match_aggregation, group_aggregation, set_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [c for c in cursor]
res_df = pd.DataFrame(data=res) # pandas Dataframe is just nice for printing in notebook

res_df

Overview on steps in project¶

project_id = '2020-07-21-passivation-trial'

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1666

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['step', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

res_df['step'].values

array(['GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad',
       'GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
       'GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
       'GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad',
       ' GromacsSolvate:ProcessAnalyzeAndVisualize:push_filepad',
       'GromacsPull:ProcessAnalyzeAndVisualize:push_filepad',
       'GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad',
       'GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad',
       'GromacsPrep:ProcessAnalyzeAndVisualize:push_filepad',
       'SphericalSurfactantPacking:ProcessAnalyzeAndVisualize:push_filepad',
       'PackingConstraintSpheres:ProcessAnalyzeAndVisualize:push_filepad',
       'SurfactantMoleculeMeasures:ProcessAnalyzeAndVisualize:push_filepad',
       'IndenterBoundingSphere:ProcessAnalyzeAndVisualize:push_filepad',
       'GromacsEnergyMinimization:GromacsEnergyMinimizationMain:push_infiles',
       'IndenterBoundingSphere:IndenterBoundingSphereMain:push_infiles',
       'GromacsRelaxation:GromacsRelaxationMain:push_infiles',
       'SphericalSurfactantPacking:push_infiles',
       'GromacsEnergyMinimizationAfterSolvation:GromacsEnergyMinimizationAfterSolvationMain:push_infiles',
       'SurfactantMoleculeMeasures:SurfactantMoleculeMeasuresMain:push_infiles',
       'GromacsNPTEquilibration:GromacsNPTEquilibrationMain:push_infiles',
       'GromacsEnergyMinimization:GromacsTrajectoryVisualization:push_infiles',
       'GromacsPullPrep:GromacsPullPrepMain:push_infiles',
       'GromacsNVTEquilibration:GromacsNVTEquilibrationMain:push_infiles'],
      dtype=object)

Overview on objects in project¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1666

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'step', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Overview on images by distinct steps¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

15

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['step', 'type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

res_df["step"][0]

'SphericalSurfactantPacking:ProcessAnalyzeAndVisualize:push_filepad'

Packing visualization¶

Indenter bounding sphere¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
    'metadata.step': {'$regex': 'IndenterBoundingSphere'}
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
        tmp.write(content)
        obj_list.append(Image(filename=tmp.name)) 
    print('.',end='')

.

for obj in obj_list:
    display(obj)

Surfactant measures¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
    'metadata.step': {'$regex': 'SurfactantMoleculeMeasures'}
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
        tmp.write(content)
        obj_list.append(Image(filename=tmp.name)) 
    print('.',end='')

.

obj_list[0]

Packing constraints¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
    'metadata.step': {'$regex': 'PackingConstraintSpheres'}
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

1

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
        tmp.write(content)
        obj_list.append(Image(filename=tmp.name)) 
    print('.',end='')

.

obj_list[0]

Packed film¶

query = {
    'metadata.project': project_id,
    'metadata.type': 'png_file',
    'metadata.step': {'$regex': 'SphericalSurfactantPacking'}
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.png') as tmp:
        tmp.write(content)
        obj_list.append(Image(filename=tmp.name)) 
    print('.',end='')

............

for obj in obj_list:
    display(obj)

Energy minimization analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

228

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad',  #{'$regex': 'GromacsEnergyMinimization'}
    "metadata.type": 'energy_file',
}
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

[ c for c in cursor]

[{'_id': {'nmolecules': 306},
  'degeneracy': 1,
  'latest': '5f164f4d7dc9cfbf44a2996f'},
 {'_id': {'nmolecules': 175},
  'degeneracy': 1,
  'latest': '5f165f3a7dc9cfbf44a2f7ff'},
 {'_id': {'nmolecules': 394},
  'degeneracy': 1,
  'latest': '5f1632947dc9cfbf44a1d052'},
 {'_id': {'nmolecules': 350},
  'degeneracy': 1,
  'latest': '5f1641167dc9cfbf44a22c0a'},
 {'_id': {'nmolecules': 438},
  'degeneracy': 1,
  'latest': '5f162f907dc9cfbf44a1cbd7'},
 {'_id': {'nmolecules': 481},
  'degeneracy': 1,
  'latest': '5f162e7b7dc9cfbf44a1c8f4'},
 {'_id': {'nmolecules': 263},
  'degeneracy': 1,
  'latest': '5f16500a7dc9cfbf44a29a3f'},
 {'_id': {'nmolecules': 525},
  'degeneracy': 1,
  'latest': '5f162d897dc9cfbf44a1c7e5'},
 {'_id': {'nmolecules': 219},
  'degeneracy': 1,
  'latest': '5f1655ce7dc9cfbf44a2b729'},
 {'_id': {'nmolecules': 131},
  'degeneracy': 1,
  'latest': '5f166fca7dc9cfbf44a36b07'},
 {'_id': {'nmolecules': 88},
  'degeneracy': 1,
  'latest': '5f1674767dc9cfbf44a39ed1'},
 {'_id': {'nmolecules': 44},
  'degeneracy': 1,
  'latest': '5f167bc17dc9cfbf44a3b977'}]

res_mi_list = []

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        em_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),em_df.index],
            names=[*c["_id"].keys(),'step'])
        em_mi_df = em_df.set_index(mi)        
        res_mi_list.append(em_mi_df)
    print('.',end='')
print('')

res_mi_df = pd.concat(res_mi_list)
res_df = res_mi_df.reset_index()

............

res_mi_df

res_df

y_quantities = [
    'Potential',
    'Pressure',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

positions = [
    (0,0),
    (0,1),
    (1,0),
    (2,0),
    (2,1),
]
fig, ax = plt.subplots(3,2,figsize=(10,12))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['step', 'type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        obj_list.append(Video.from_file(tmp.name))
    print('.',end='')

............

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
    {
        "$sort": { 
            "_id.nmolecules": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_dict = {}
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    # print(metadata['metadata'])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        key = tuple(c["_id"].values())
        obj_dict.update({key: Video.from_file(tmp.name)})
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

(525,)

(481,)

(438,)

(394,)

(350,)

(306,)

(263,)

(219,)

(175,)

(131,)

Pulling analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsPull:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

276

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

The gmx energy table:

  1  Restraint-Pot.   2  U-B              3  Proper-Dih.      4  LJ-14         
  5  Coulomb-14       6  LJ-(SR)          7  Coulomb-(SR)     8  Coul.-recip.  
  9  Position-Rest.  10  COM-Pull-En.    11  Potential       12  Kinetic-En.   
 13  Total-Energy    14  Temperature     15  Pressure        16  Constr.-rmsd  
 17  Vir-XX          18  Vir-XY          19  Vir-XZ          20  Vir-YX        
 21  Vir-YY          22  Vir-YZ          23  Vir-ZX          24  Vir-ZY        
 25  Vir-ZZ          26  Pres-XX         27  Pres-XY         28  Pres-XZ       
 29  Pres-YX         30  Pres-YY         31  Pres-YZ         32  Pres-ZX       
 33  Pres-ZY         34  Pres-ZZ         35  #Surf*SurfTen   36  T-rest

converted to dict with regex

 \s+([0-9]+)\s+([^\s]+)

and replacement

 '$2': $1,\n

gmx_energy_dict = {
    'Restraint-Pot.': 1,
    'U-B': 2,
    'Proper-Dih.': 3,
    'LJ-14': 4,
    'Coulomb-14': 5,
    'LJ-(SR)': 6,
    'Coulomb-(SR)': 7,
    'Coul.-recip.': 8,
    'Position-Rest.': 9,
    'COM-Pull-En.': 10,
    'Potential': 11,
    'Kinetic-En.': 12,
    'Total-Energy': 13,
    'Temperature': 14,
    'Pressure': 15,
    'Constr.-rmsd': 16,
    'Vir-XX': 17,
    'Vir-XY': 18,
    'Vir-XZ': 19,
    'Vir-YX': 20,
    'Vir-YY': 21,
    'Vir-YZ': 22,
    'Vir-ZX': 23,
    'Vir-ZY': 24,
    'Vir-ZZ': 25,
    'Pres-XX': 26,
    'Pres-XY': 27,
    'Pres-XZ': 28,
    'Pres-YX': 29,
    'Pres-YY': 30,
    'Pres-YZ': 31,
    'Pres-ZX': 32,
    'Pres-ZY': 33,
    'Pres-ZZ': 34,
    '#Surf*SurfTen': 35,
    'T-rest': 36,
}

query = { 
    "metadata.project": project_id,
    "metadata.type":    'energy_file',
    "metadata.step": "GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

gmx_energy_selection = [
    'Restraint-Pot.',
    'Position-Rest.',
    'COM-Pull-En.',
    'Potential',
    'Kinetic-En.',
    'Total-Energy',
    'Temperature',
    'Pressure',
    'Constr.-rmsd',
]

res_list = []
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

res_df_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    
    #df = panedr.edr_to_df(tmp.name), fails
    tmpin = tempfile.NamedTemporaryFile(mode='w+b',suffix='.edr', delete=False)
    
    # cur_res_dict = {}
    with tmpin:
        tmpin.write(content)
        #tmpin.seek(0)
       
    res_df = None
    for sel in gmx_energy_selection:  
        try:
            tmpout = tempfile.NamedTemporaryFile(suffix='.xvg', delete=False)
            res = gromacs.energy(f=tmpin.name,o=tmpout.name,
                                 input=str(gmx_energy_dict[sel]))
            #with open(tmpout.name,'r') as f:
            #    xvg = f.read()
            #tmpout.delete()
            xvg = mda.auxiliary.XVG.XVGReader(tmpout.name)
            xvg_time = xvg.read_all_times()
            xvg_data = np.array([ f.data[1:] for f in xvg ]).flatten() # 1st entry contains times
            os.unlink(tmpout.name)
        except: 
            logger.warning("Failed to read '{:s}' from data set {:d}.".format(sel,i))
            failed_list.append((nmolecules, sel))
        else:
            r = {'nmolecules': [nmolecules]*len(xvg_time), 'time': xvg_time, sel: xvg_data}
            cur_df = pd.DataFrame(r)
            if res_df is None:
                res_df = cur_df
            else:
                res_df = pd.merge(res_df, cur_df, how='outer', on=['nmolecules', 'time'])
    res_df_list.append(res_df)
    os.unlink(tmpin.name)
    print('.',end='')
print('')
res_df = pd.concat(res_df_list)
res_df_mi = res_df.set_index(['nmolecules','time'])

............

res_df_mi

cols = 2
y_quantities = [
    'Restraint-Pot.',
    'Position-Rest.',
    'COM-Pull-En.',
    'Potential',
    'Kinetic-En.',
    'Total-Energy',
    'Temperature',
    'Pressure',
    'Constr.-rmsd',
    ]
n = len(y_quantities)
rows = round(n/cols)
positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Pulling forces¶

res_df_list = []
failed_list = []

query = { 
    "metadata.project": project_id,
    "metadata.step": "GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
    "metadata.type": 'pullf_file',
}

fp.filepad.count_documents(query)
match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    
    tmpin = tempfile.NamedTemporaryFile(mode='w+b',suffix='.xvg', delete=False)
    
    with tmpin:
        tmpin.write(content)
        
    try:
        xvg = mda.auxiliary.XVG.XVGReader(tmpin.name)
        xvg_time = xvg.read_all_times()
        xvg_data = np.array([ f.data[1:] for f in xvg ])# .flatten() # 1st entry contains times
    except: 
        logger.warning("Failed to read data set {:d}.".format(i))
        failed_list.append(nmolecules)
    else:
        res_df_list.append(pd.DataFrame({
            'nmolecules': np.array([nmolecules]*len(xvg_time), dtype=int),
            'time': xvg_time, 
            **{i: xvg_data[:,i] for i in range(nmolecules)}
        }))
    os.unlink(tmpin.name)
    print('.',end='')
print('')
res_df = pd.concat(res_df_list)
res_df_mi = res_df.set_index(['nmolecules','time'])

............

# pulling forces
n = len(res_df['nmolecules'].unique())
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
for pos, (key, grp) in zip(positions,res_df.groupby(['nmolecules'])):
    columns = list(set(grp.columns) - set(['nmolecules','time']))
    grp.plot('time', columns, ax=ax[pos],title=key,legend=None)
fig.tight_layout()

# mean pulling force
fig, ax = plt.subplots(1,1,figsize=(5,4))
for key, grp in res_df.groupby(['nmolecules']):
    columns = list(set(grp.columns) - set(['nmolecules','time']))
    grp = grp.set_index('time')
    grp = grp.drop(columns='nmolecules')
    grp.mean(axis=1).plot(legend=True, label=key, ax=ax)
fig.tight_layout()
#fig.legend()

Pulling groups movement¶

res_df_list = []
failed_list = []

query = { 
    "metadata.project": project_id,
    "metadata.step": "GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
    "metadata.type":    'pullx_file',
}

fp.filepad.count_documents(query)
match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)


for i, c in enumerate(cursor): 
    print(c["_id"])
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])  # int(metadata["metadata"]["nmolecules"])
    
    tmpin = tempfile.NamedTemporaryFile(mode='w+b',suffix='.xvg', delete=False)
    
    with tmpin:
        tmpin.write(content)
        
    try:
        xvg = gromacs.fileformats.XVG(tmpin.name)
        xvg_time = xvg.array[0,:]
        
        #xvg_labels = ['1', '1 ref', '1 dX', '1 dY', '1 dZ', '1 g 1 X', '1 g 1 Y', '1 g 1 Z', '1 g 2 X', '1 g 2 Y', '1 g 2 Z']
        N_pull_coords = nmolecules
        N_cols = len(xvg.names)
        N_cols_per_coord = int(N_cols / N_pull_coords)
        
        xvg_labels = xvg.names[:N_cols_per_coord]
        xvg_data = {}
        for j in range(N_pull_coords):
            for k in range(N_cols_per_coord):
                xvg_data[(j,xvg_labels[k])] = xvg.array[
                    1+j*N_cols_per_coord+k,:]
        
    except: 
        logger.exception("Failed to read data set {:d}.".format(i))
        failed_list.append(nmolecules)

    else:
        res_df_list.append(pd.DataFrame({
            'nmolecules': np.array([nmolecules]*len(xvg_time), dtype=int),
            'time': xvg_time, 
            **xvg_data # {i: xvg_data[:,i] for i in range(nmolecules)}
        }))
    os.unlink(tmpin.name)
res_df = pd.concat(res_df_list)
res_df_mi = res_df.set_index(['nmolecules','time'])
res_df_mi.columns = pd.MultiIndex.from_tuples(res_df_mi.columns, names=['nmolecule', 'coord'])

{'nmolecules': 525}
{'nmolecules': 394}
{'nmolecules': 438}
{'nmolecules': 350}
{'nmolecules': 263}
{'nmolecules': 175}
{'nmolecules': 131}
{'nmolecules': 44}
{'nmolecules': 306}
{'nmolecules': 88}
{'nmolecules': 481}
{'nmolecules': 219}

res_df_mi

res_df = res_df_mi.groupby(axis=1,level='coord').mean().reset_index()

res_df

cols = 2
y_quantities = [
    '1', 
    '1 ref', 
    '1 dX', 
    '1 dY', 
    '1 dZ', 
    '1 g 1 X', 
    '1 g 1 Y', 
    '1 g 1 Z', 
    '1 g 2 X', 
    '1 g 2 Y', 
    '1 g 2 Z'
]
n = len(y_quantities)
rows = round(n/cols)
positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsPull:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

# check files degenerate by 'metadata.type' ad 'metadata.name'

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
                'step': '$metadata.step',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    },
    {
        "$sort": { 
            "_id.nmolecules": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['step', 'type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["gfs_id"])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        obj_list.append(Video.from_file(tmp.name))
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

(525,)

(481,)

(438,)

(394,)

(350,)

(306,)

(263,)

(219,)

(175,)

(131,)

Pre-evaluated RDF¶

Overview¶

query = { 
    "metadata.project": project_id,
    "metadata.type": {'$regex': '.*rdf$'},
    "metadata.step": "GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

120

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Substrate - surfactant head RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_surfactant_head_rdf',
    "metadata.step": "GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Substrate - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_surfactant_tail_rdf',
    "metadata.step": "GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
    
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Surfactant head - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'surfactant_head_surfactant_tail_rdf',
    "metadata.step": "GromacsPull:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Energy minimization after solvation analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

240

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad',  #{'$regex': 'GromacsEnergyMinimization'}
    "metadata.type": 'energy_file',
}
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res_mi_list = []

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        em_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),em_df.index],
            names=[*c["_id"].keys(),'step'])
        em_mi_df = em_df.set_index(mi)        
        res_mi_list.append(em_mi_df)
    print('.',end='')
print('')

res_mi_df = pd.concat(res_mi_list)
res_df = res_mi_df.reset_index()

............

res_mi_df

y_quantities = [
    'Potential',
    'Pressure',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

positions = [
    (0,0),
    (0,1),
    (1,0),
    (2,0),
    (2,1),
]
fig, ax = plt.subplots(3,2,figsize=(10,12))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

# check

aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'nmolecules': '$metadata.system.surfactant.nmolecules'
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

# for i, c in enumerate(cursor): 
#    content, metadata = fp.get_file_by_id(c["latest"])
#    nmolecules = int(c["_id"]["nmolecules"])
    

res = [ {**c['_id'], **c} for c in cursor]
columns = ['nmolecules', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
    {
        "$sort": { 
            "_id.nmolecules": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_dict = {}
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    # print(metadata['metadata'])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        obj_dict.update({metadata['metadata']['system']['surfactant']['nmolecules']: Video.from_file(tmp.name)})
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

NVT equilibration analysis¶

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

267

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
    "metadata.type":    'energy_file',
}
fp.filepad.count_documents(query)

13

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

[ c for c in cursor]

[{'_id': {'nmolecules': 306},
  'degeneracy': 2,
  'latest': '5f165d3f7dc9cfbf44a2ea4a'},
 {'_id': {'nmolecules': 44},
  'degeneracy': 1,
  'latest': '5f16903c7dc9cfbf44a43d45'},
 {'_id': {'nmolecules': 525},
  'degeneracy': 1,
  'latest': '5f1639b37dc9cfbf44a1e5df'},
 {'_id': {'nmolecules': 88},
  'degeneracy': 1,
  'latest': '5f1683b27dc9cfbf44a3f82d'},
 {'_id': {'nmolecules': 481},
  'degeneracy': 1,
  'latest': '5f163aa37dc9cfbf44a1f29d'},
 {'_id': {'nmolecules': 438},
  'degeneracy': 1,
  'latest': '5f163bc07dc9cfbf44a1ff51'},
 {'_id': {'nmolecules': 219},
  'degeneracy': 1,
  'latest': '5f166e357dc9cfbf44a35e51'},
 {'_id': {'nmolecules': 394},
  'degeneracy': 1,
  'latest': '5f164d8c7dc9cfbf44a28653'},
 {'_id': {'nmolecules': 350},
  'degeneracy': 1,
  'latest': '5f1654757dc9cfbf44a2aa69'},
 {'_id': {'nmolecules': 263},
  'degeneracy': 1,
  'latest': '5f1661d17dc9cfbf44a2f897'},
 {'_id': {'nmolecules': 175},
  'degeneracy': 1,
  'latest': '5f16721a7dc9cfbf44a38485'},
 {'_id': {'nmolecules': 131},
  'degeneracy': 1,
  'latest': '5f167fe07dc9cfbf44a3d2d3'}]

res_list = []

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        res_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),res_df.index],
            names=[*c["_id"].keys(),'step'])
        res_mi_df = res_df.set_index(mi)
        res_list.append(res_mi_df)
    print('.',end='')
print('')
res_df_mi = pd.concat(res_list)
res_df = res_df_mi.reset_index()

............

res_df.columns

Index(['nmolecules', 'step', 'Time', 'Bond', 'U-B', 'Proper Dih.', 'LJ-14',
       'Coulomb-14', 'LJ (SR)', 'Coulomb (SR)', 'Coul. recip.',
       'Position Rest.', 'Potential', 'Kinetic En.', 'Total Energy',
       'Conserved En.', 'Temperature', 'Pressure', 'Constr. rmsd', 'Vir-XX',
       'Vir-XY', 'Vir-XZ', 'Vir-YX', 'Vir-YY', 'Vir-YZ', 'Vir-ZX', 'Vir-ZY',
       'Vir-ZZ', 'Pres-XX', 'Pres-XY', 'Pres-XZ', 'Pres-YX', 'Pres-YY',
       'Pres-YZ', 'Pres-ZX', 'Pres-ZY', 'Pres-ZZ', '#Surf*SurfTen',
       'Coul-SR:Surfactant-Surfactant', 'LJ-SR:Surfactant-Surfactant',
       'Coul-14:Surfactant-Surfactant', 'LJ-14:Surfactant-Surfactant',
       'Coul-SR:Surfactant-Substrate', 'LJ-SR:Surfactant-Substrate',
       'Coul-14:Surfactant-Substrate', 'LJ-14:Surfactant-Substrate',
       'Coul-SR:Surfactant-Ion', 'LJ-SR:Surfactant-Ion',
       'Coul-14:Surfactant-Ion', 'LJ-14:Surfactant-Ion',
       'Coul-SR:Surfactant-rest', 'LJ-SR:Surfactant-rest',
       'Coul-14:Surfactant-rest', 'LJ-14:Surfactant-rest',
       'Coul-SR:Substrate-Substrate', 'LJ-SR:Substrate-Substrate',
       'Coul-14:Substrate-Substrate', 'LJ-14:Substrate-Substrate',
       'Coul-SR:Substrate-Ion', 'LJ-SR:Substrate-Ion', 'Coul-14:Substrate-Ion',
       'LJ-14:Substrate-Ion', 'Coul-SR:Substrate-rest', 'LJ-SR:Substrate-rest',
       'Coul-14:Substrate-rest', 'LJ-14:Substrate-rest', 'Coul-SR:Ion-Ion',
       'LJ-SR:Ion-Ion', 'Coul-14:Ion-Ion', 'LJ-14:Ion-Ion', 'Coul-SR:Ion-rest',
       'LJ-SR:Ion-rest', 'Coul-14:Ion-rest', 'LJ-14:Ion-rest',
       'Coul-SR:rest-rest', 'LJ-SR:rest-rest', 'Coul-14:rest-rest',
       'LJ-14:rest-rest', 'T-non-Substrate', 'T-Substrate',
       'Lamb-non-Substrate', 'Lamb-Substrate'],
      dtype='object')

res_df_mi

#n = len(res_df['nmolecules'].unique())
y_quantities = [
    'Temperature',
    'Pressure',
    'Potential',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

n = len(y_quantities)
cols = 2
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
    {
        "$sort": { 
            "_id.nmolecules": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_dict = {}
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    # print(metadata['metadata'])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        key = tuple(c["_id"].values())
        obj_dict.update({key: Video.from_file(tmp.name)})
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

(525,)

(481,)

(438,)

(394,)

(350,)

(306,)

(263,)

(219,)

(175,)

(131,)

NPT equilibration analysis¶

Datasets in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad'
}

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'dataset': '$metadata.step_specific.dtool_push.remote_dataset',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id']['dataset'], **c} for c in cursor]
columns = ['uuid', 'name', 'uri', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Overview on objects in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad'
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

252

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
    "metadata.type":    'energy_file',
}
fp.filepad.count_documents(query)

12

metadata = fp.filepad.find_one(query)

metadata

{'_id': ObjectId('5f163f547dc9cfbf44a20c08'),
 'identifier': '/p/scratch/chfr13/hoermann/fireworks/launchpad/launcher_2020-07-21-01-05-21-471340/default.edr',
 'original_file_name': 'default.edr',
 'original_file_path': '/p/scratch/chfr13/hoermann/fireworks/launchpad/launcher_2020-07-21-01-05-21-471340/default.edr',
 'metadata': {'project': '2020-07-21-passivation-trial',
  'datetime': '2020-07-21 01:13:13.743275',
  'step': 'GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
  'description': 'Parametric trial runs for indenter passivation',
  'owners': [{'name': 'Johannes Laurin Hörmann',
    'email': 'johannes.hoermann@imtek.uni-freiburg.de',
    'username': 'fr_jh1130',
    'orcid': '0000-0001-5867-695X'}],
  'machine': 'juwels_devel',
  'parameter_label_key_dict': {'n': 'system->surfactant->nmolecules',
   'm': 'system->counterion->nmolecules'},
  'parameter_values': [{'n': 44, 'm': 44},
   {'n': 88, 'm': 88},
   {'n': 131, 'm': 131},
   {'n': 175, 'm': 175},
   {'n': 219, 'm': 219},
   {'n': 263, 'm': 263},
   {'n': 306, 'm': 306},
   {'n': 350, 'm': 350},
   {'n': 394, 'm': 394},
   {'n': 438, 'm': 438},
   {'n': 481, 'm': 481},
   {'n': 525, 'm': 525}],
  'mode': 'trial',
  'system': {'counterion': {'name': 'NA',
    'resname': 'NA',
    'nmolecules': 525,
    'reference_atom': {'name': 'NA'}},
   'surfactant': {'name': 'SDS',
    'resname': 'SDS',
    'nmolecules': 525,
    'connector_atom': {'index': 2, 'position': [3.47, 0.7, 20.88]},
    'head_atom': {'name': 'S'},
    'tail_atom': {'name': 'C12'},
    'bounding_sphere': {'center': [1.885, 0.545, 15.47],
     'radius': 7.758430898061799,
     'radius_connector_atom': 5.639534555262516},
    'head_group': {'diameter': 2.1188963427992826}},
   'substrate': {'name': 'AUM',
    'resname': 'AUM',
    'natoms': 3873,
    'reference_atom': {'name': 'AU'}},
   'solvent': {'name': 'H2O',
    'resname': 'SOL',
    'reference_atom': {'name': 'OW'}},
   'indenter': {'bounding_sphere': {'center': [-0.02151743007486928,
      -0.17756268039515888,
      0.46946045857874186],
     'radius': 26.390609083217864}}},
  'step_specific': {'packing': {'surfactant_indenter': {'outer_atom_index': 1,
     'inner_atom_index': 39,
     'tolerance': 2,
     'constraints': {'R_outer': 45.90747087934146,
      'R_outer_constraint': 43.90747087934146,
      'R_inner_constraint': 30.509505426017146,
      'R_inner': 28.390609083217864}}},
   'pulling': {'pull_atom_name': 'C12',
    'spring_constant': 10000,
    'rate': -0.1,
    'nsteps': 1000},
   'dtool_push': {'dtool_target': '/p/project/chfr13/hoermann4/dtool/DATASETS',
    'remote_dataset': {'uri': 'file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-736370-n-525-m-525-gromacsnptequilibration',
     'uuid': '71a46b71-f815-4c3b-8671-01eefa3efdae',
     'name': '2020-07-21-01-13-13-736370-n-525-m-525-gromacsnptequilibration'},
    'local_proto_dataset': {'uri': 'file://juwels01.ib.juwels.fzj.de/p/scratch/chfr13/hoermann/fireworks/launchpad/launcher_2020-07-21-01-05-03-680344/2020-07-21-01-13-13-736370-n-525-m-525-gromacsnptequilibration',
     'uuid': '71a46b71-f815-4c3b-8671-01eefa3efdae',
     'name': '2020-07-21-01-13-13-736370-n-525-m-525-gromacsnptequilibration'},
    'local_frozen_dataset': {'uri': 'file://juwels01.ib.juwels.fzj.de/p/scratch/chfr13/hoermann/fireworks/launchpad/launcher_2020-07-21-01-05-03-680344/2020-07-21-01-13-13-736370-n-525-m-525-gromacsnptequilibration',
     'uuid': '71a46b71-f815-4c3b-8671-01eefa3efdae',
     'name': '2020-07-21-01-13-13-736370-n-525-m-525-gromacsnptequilibration'}}},
  'project_id': '2020-07-21-passivation-trial',
  'creation_date': '2020-07-21 01:12:45.402090',
  'expiration_date': '2022-07-21 01:12:45.402090',
  'type': 'energy_file'},
 'compressed': True,
 'gfs_id': '5f163f547dc9cfbf44a20c06'}

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

[ c for c in cursor]

[{'_id': {'nmolecules': 350},
  'degeneracy': 1,
  'latest': '5f165a437dc9cfbf44a2be2e'},
 {'_id': {'nmolecules': 525},
  'degeneracy': 1,
  'latest': '5f163f547dc9cfbf44a20c06'},
 {'_id': {'nmolecules': 263},
  'degeneracy': 1,
  'latest': '5f16675b7dc9cfbf44a3326e'},
 {'_id': {'nmolecules': 481},
  'degeneracy': 1,
  'latest': '5f16404e7dc9cfbf44a21f30'},
 {'_id': {'nmolecules': 306},
  'degeneracy': 1,
  'latest': '5f1664077dc9cfbf44a31e3e'},
 {'_id': {'nmolecules': 219},
  'degeneracy': 1,
  'latest': '5f16739d7dc9cfbf44a39206'},
 {'_id': {'nmolecules': 175},
  'degeneracy': 1,
  'latest': '5f1677d37dc9cfbf44a3a04d'},
 {'_id': {'nmolecules': 131},
  'degeneracy': 1,
  'latest': '5f1685257dc9cfbf44a40545'},
 {'_id': {'nmolecules': 88},
  'degeneracy': 1,
  'latest': '5f16895f7dc9cfbf44a411e7'},
 {'_id': {'nmolecules': 44},
  'degeneracy': 1,
  'latest': '5f1695b27dc9cfbf44a4620d'},
 {'_id': {'nmolecules': 438},
  'degeneracy': 1,
  'latest': '5f16414e7dc9cfbf44a22cd5'},
 {'_id': {'nmolecules': 394},
  'degeneracy': 1,
  'latest': '5f1653627dc9cfbf44a29da0'}]

res_list = []

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        res_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),res_df.index],
            names=[*c["_id"].keys(),'step'])
        res_mi_df = res_df.set_index(mi)
        res_list.append(res_mi_df)
    print('.',end='')
print('')
res_df_mi = pd.concat(res_list)
res_df = res_df_mi.reset_index()

............

res_df.columns

Index(['nmolecules', 'step', 'Time', 'Bond', 'U-B', 'Proper Dih.', 'LJ-14',
       'Coulomb-14', 'LJ (SR)', 'Coulomb (SR)', 'Coul. recip.',
       'Position Rest.', 'Potential', 'Kinetic En.', 'Total Energy',
       'Conserved En.', 'Temperature', 'Pressure', 'Constr. rmsd', 'Box-X',
       'Box-Y', 'Box-Z', 'Volume', 'Density', 'pV', 'Enthalpy', 'Vir-XX',
       'Vir-XY', 'Vir-XZ', 'Vir-YX', 'Vir-YY', 'Vir-YZ', 'Vir-ZX', 'Vir-ZY',
       'Vir-ZZ', 'Pres-XX', 'Pres-XY', 'Pres-XZ', 'Pres-YX', 'Pres-YY',
       'Pres-YZ', 'Pres-ZX', 'Pres-ZY', 'Pres-ZZ', '#Surf*SurfTen',
       'Coul-SR:Surfactant-Surfactant', 'LJ-SR:Surfactant-Surfactant',
       'Coul-14:Surfactant-Surfactant', 'LJ-14:Surfactant-Surfactant',
       'Coul-SR:Surfactant-Substrate', 'LJ-SR:Surfactant-Substrate',
       'Coul-14:Surfactant-Substrate', 'LJ-14:Surfactant-Substrate',
       'Coul-SR:Surfactant-Ion', 'LJ-SR:Surfactant-Ion',
       'Coul-14:Surfactant-Ion', 'LJ-14:Surfactant-Ion',
       'Coul-SR:Surfactant-rest', 'LJ-SR:Surfactant-rest',
       'Coul-14:Surfactant-rest', 'LJ-14:Surfactant-rest',
       'Coul-SR:Substrate-Substrate', 'LJ-SR:Substrate-Substrate',
       'Coul-14:Substrate-Substrate', 'LJ-14:Substrate-Substrate',
       'Coul-SR:Substrate-Ion', 'LJ-SR:Substrate-Ion', 'Coul-14:Substrate-Ion',
       'LJ-14:Substrate-Ion', 'Coul-SR:Substrate-rest', 'LJ-SR:Substrate-rest',
       'Coul-14:Substrate-rest', 'LJ-14:Substrate-rest', 'Coul-SR:Ion-Ion',
       'LJ-SR:Ion-Ion', 'Coul-14:Ion-Ion', 'LJ-14:Ion-Ion', 'Coul-SR:Ion-rest',
       'LJ-SR:Ion-rest', 'Coul-14:Ion-rest', 'LJ-14:Ion-rest',
       'Coul-SR:rest-rest', 'LJ-SR:rest-rest', 'Coul-14:rest-rest',
       'LJ-14:rest-rest', 'T-non-Substrate', 'T-Substrate',
       'Lamb-non-Substrate', 'Lamb-Substrate'],
      dtype='object')

res_df_mi

#n = len(res_df['nmolecules'].unique())
y_quantities = [
    'Temperature',
    'Pressure',
    'Volume',
    'Potential',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

n = len(y_quantities)
cols = 2
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
    {
        "$sort": { 
            "_id.nmolecules": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_dict = {}
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    # print(metadata['metadata'])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        key = tuple(c["_id"].values())
        obj_dict.update({key: Video.from_file(tmp.name)})
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

(525,)

(481,)

(438,)

(394,)

(350,)

(306,)

(263,)

(219,)

(175,)

(131,)

Pre-evaluated RDF¶

Overview¶

query = { 
    "metadata.project": project_id,
    "metadata.type": {'$regex': '.*rdf$'},
    "metadata.step": "GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

120

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Substrate - surfactant head RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_surfactant_head_rdf',
    "metadata.step": "GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Substrate - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_surfactant_tail_rdf',
    "metadata.step": "GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
    
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Surfactant head - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'surfactant_head_surfactant_tail_rdf',
    "metadata.step": "GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Substrate - substrate RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_substrate_rdf',
    "metadata.step": "GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
cutoff_index_inner = 1
cutoff_index_outer = -1
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][0][cutoff_index_inner:cutoff_index_outer], label='First frame RDF')
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][len(data)//2][cutoff_index_inner:cutoff_index_outer],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][-1][cutoff_index_inner:cutoff_index_outer],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Relaxation analysis¶

Datasets in step¶

# queries to the data base are simple dictionaries
query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad'
}

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'dataset': '$metadata.step_specific.dtool_push.remote_dataset',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id']['dataset'], **c} for c in cursor]
columns = ['uuid', 'name', 'uri', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Overview on objects in step¶

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

252

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
                'name': '$metadata.name',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'name', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Global observables¶

query = { 
    "metadata.project": project_id,
    'metadata.step': 'GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad',
    "metadata.type":    'energy_file',
}
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

[ c for c in cursor]

[{'_id': {'nmolecules': 525},
  'degeneracy': 1,
  'latest': '5f1649aa7dc9cfbf44a23b2a'},
 {'_id': {'nmolecules': 394},
  'degeneracy': 1,
  'latest': '5f165c1c7dc9cfbf44a2d143'},
 {'_id': {'nmolecules': 481},
  'degeneracy': 1,
  'latest': '5f164b077dc9cfbf44a25445'},
 {'_id': {'nmolecules': 350},
  'degeneracy': 1,
  'latest': '5f1662ee7dc9cfbf44a30542'},
 {'_id': {'nmolecules': 263},
  'degeneracy': 1,
  'latest': '5f166fe67dc9cfbf44a36b92'},
 {'_id': {'nmolecules': 306},
  'degeneracy': 1,
  'latest': '5f166b7b7dc9cfbf44a34573'},
 {'_id': {'nmolecules': 219},
  'degeneracy': 1,
  'latest': '5f167c137dc9cfbf44a3b9e9'},
 {'_id': {'nmolecules': 131},
  'degeneracy': 1,
  'latest': '5f168d8a7dc9cfbf44a4248a'},
 {'_id': {'nmolecules': 88},
  'degeneracy': 1,
  'latest': '5f16922a7dc9cfbf44a449a3'},
 {'_id': {'nmolecules': 44},
  'degeneracy': 1,
  'latest': '5f169e197dc9cfbf44a46e80'},
 {'_id': {'nmolecules': 438},
  'degeneracy': 1,
  'latest': '5f164c4a7dc9cfbf44a26d4a'},
 {'_id': {'nmolecules': 175},
  'degeneracy': 1,
  'latest': '5f1680ad7dc9cfbf44a3df61'}]

res_list = []

cursor = fp.filepad.aggregate(aggregation_pipeline)

for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    with tempfile.NamedTemporaryFile(suffix='.edr') as tmp:
        tmp.write(content)
        res_df = panedr.edr_to_df(tmp.name)
        
        mi = pd.MultiIndex.from_product(
            [c["_id"].values(),res_df.index],
            names=[*c["_id"].keys(),'step'])
        res_mi_df = res_df.set_index(mi)
        res_list.append(res_mi_df)
    print('.',end='')
print('')
res_df_mi = pd.concat(res_list)
res_df = res_df_mi.reset_index()

............

res_df.columns

Index(['nmolecules', 'step', 'Time', 'Bond', 'U-B', 'Proper Dih.', 'LJ-14',
       'Coulomb-14', 'LJ (SR)', 'Coulomb (SR)', 'Coul. recip.',
       'Position Rest.', 'Potential', 'Kinetic En.', 'Total Energy',
       'Conserved En.', 'Temperature', 'Pressure', 'Constr. rmsd', 'Box-X',
       'Box-Y', 'Box-Z', 'Volume', 'Density', 'pV', 'Enthalpy', 'Vir-XX',
       'Vir-XY', 'Vir-XZ', 'Vir-YX', 'Vir-YY', 'Vir-YZ', 'Vir-ZX', 'Vir-ZY',
       'Vir-ZZ', 'Pres-XX', 'Pres-XY', 'Pres-XZ', 'Pres-YX', 'Pres-YY',
       'Pres-YZ', 'Pres-ZX', 'Pres-ZY', 'Pres-ZZ', '#Surf*SurfTen',
       'Box-Vel-XX', 'Box-Vel-YY', 'Box-Vel-ZZ',
       'Coul-SR:Surfactant-Surfactant', 'LJ-SR:Surfactant-Surfactant',
       'Coul-14:Surfactant-Surfactant', 'LJ-14:Surfactant-Surfactant',
       'Coul-SR:Surfactant-Substrate', 'LJ-SR:Surfactant-Substrate',
       'Coul-14:Surfactant-Substrate', 'LJ-14:Surfactant-Substrate',
       'Coul-SR:Surfactant-Ion', 'LJ-SR:Surfactant-Ion',
       'Coul-14:Surfactant-Ion', 'LJ-14:Surfactant-Ion',
       'Coul-SR:Surfactant-rest', 'LJ-SR:Surfactant-rest',
       'Coul-14:Surfactant-rest', 'LJ-14:Surfactant-rest',
       'Coul-SR:Substrate-Substrate', 'LJ-SR:Substrate-Substrate',
       'Coul-14:Substrate-Substrate', 'LJ-14:Substrate-Substrate',
       'Coul-SR:Substrate-Ion', 'LJ-SR:Substrate-Ion', 'Coul-14:Substrate-Ion',
       'LJ-14:Substrate-Ion', 'Coul-SR:Substrate-rest', 'LJ-SR:Substrate-rest',
       'Coul-14:Substrate-rest', 'LJ-14:Substrate-rest', 'Coul-SR:Ion-Ion',
       'LJ-SR:Ion-Ion', 'Coul-14:Ion-Ion', 'LJ-14:Ion-Ion', 'Coul-SR:Ion-rest',
       'LJ-SR:Ion-rest', 'Coul-14:Ion-rest', 'LJ-14:Ion-rest',
       'Coul-SR:rest-rest', 'LJ-SR:rest-rest', 'Coul-14:rest-rest',
       'LJ-14:rest-rest', 'T-non-Substrate', 'T-Substrate',
       'Lamb-non-Substrate', 'Lamb-Substrate'],
      dtype='object')

res_df_mi

#n = len(res_df['nmolecules'].unique())
y_quantities = [
    'Temperature',
    'Pressure',
    'Volume',
    'Potential',
    'Bond',
    'Coulomb (SR)',
    'Coul. recip.',
    ]

n = len(y_quantities)
cols = 2
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
for key, grp in res_df.groupby(['nmolecules']):
    for y_quantity, position in zip(y_quantities, positions):
        grp.plot('Time',y_quantity,ax=ax[position],label=key,title=y_quantity)
        
fig.tight_layout()

Visualize trajectory¶

query = {
    'metadata.project': project_id,
    'metadata.step': 'GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad',
    'metadata.type': 'mp4_file',
}

# use underlying MongoDB functionality to check total number of documents matching query
fp.filepad.count_documents(query)

12

parameter_dict = {'nmolecules': 'metadata.system.surfactant.nmolecules'}

aggregation_pipeline = [
    {
        "$match": query
    },
    {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    },
    { 
        "$group": { 
            "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
            "degeneracy": {"$sum": 1}, # number matching data sets
            "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
        }
    },
    {
        "$sort": { 
            "_id.nmolecules": pymongo.DESCENDING,
        }
    },
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

obj_dict = {}
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    # print(metadata['metadata'])
    with tempfile.NamedTemporaryFile(suffix='.mp4',delete=False) as tmp:
        tmp.write(content)
        # obj_list.append(Video(filename=tmp.name)) 
        # obj_list.append(tmp.name)
        key = tuple(c["_id"].values())
        obj_dict.update({key: Video.from_file(tmp.name)})
    print('.',end='')

............

for key, obj in obj_dict.items():
    print(key)
    display(obj)

(525,)

(481,)

(438,)

(394,)

(350,)

(306,)

(263,)

(219,)

(175,)

(131,)

Pre-evaluated RDF¶

Overview¶

query = { 
    "metadata.project": project_id,
    "metadata.type": {'$regex': '.*rdf$'},
    "metadata.step": "GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

120

# check files degenerate by 'metadata.type' ad 'metadata.name'
aggregation_pipeline = [
    {
        "$match": query
    },
    {  # group by unique project id
        "$group": { 
            "_id": { 
                'type': '$metadata.type',
            },
            "object_count": {"$sum": 1}, # count matching data sets
            "earliest":  {'$min': '$metadata.datetime' },
            "latest":  {'$max': '$metadata.datetime' },
        },
    },
    {  # sort by earliest date, descending
        "$sort": { 
            "earliest": pymongo.DESCENDING,
        }
    }
]

cursor = fp.filepad.aggregate(aggregation_pipeline)

res = [ {**c['_id'], **c} for c in cursor]
columns = ['type', 'earliest', 'latest', 'object_count', '_id']
res_df = pd.DataFrame(data=res, columns=columns) # pandas Dataframe is just nice for printing in notebook
del res_df["_id"]

res_df

Substrate - surfactant head RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_surfactant_head_rdf',
    "metadata.step": "GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
aggregation_pipeline = [ match_aggregation, sort_aggregation, group_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Substrate - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_surfactant_tail_rdf',
    "metadata.step": "GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
    
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Surfactant head - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'surfactant_head_surfactant_tail_rdf',
    "metadata.step": "GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Surfactant head - counterion RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'counterion_surfactant_head_rdf',
    "metadata.step": "GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'],data['rdf'][0], label='First frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][len(data)//2],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'],data['rdf'][-1],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Surfactant head - surfactant head RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'surfactant_head_surfactant_head_rdf',
    "metadata.step": "GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
cutoff_index_inner = 1
cutoff_index_outer = -1
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'][cutoff_index_inner:],data['rdf'][0][cutoff_index_inner:], label='First frame RDF')
    ax[pos].plot(data['dist'][cutoff_index_inner:],data['rdf'][len(data)//2][cutoff_index_inner:],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'][cutoff_index_inner:],data['rdf'][-1][cutoff_index_inner:],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Surfactant tail - surfactant tail RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'surfactant_tail_surfactant_tail_rdf',
    "metadata.step": "GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
cutoff_index_inner = 1
cutoff_index_outer = -1
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][0][cutoff_index_inner:cutoff_index_outer], label='First frame RDF')
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][len(data)//2][cutoff_index_inner:cutoff_index_outer],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][-1][cutoff_index_inner:cutoff_index_outer],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

Substrate - substrate RDF¶

parameter_dict = {
    'nmolecules': 'metadata.system.surfactant.nmolecules'}

query = { 
    "metadata.project": project_id,
    "metadata.type": 'substrate_substrate_rdf',
    "metadata.step": "GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad",
}

fp.filepad.count_documents(query)

12

res_dict = {}
failed_list = []

match_aggregation = {
        "$match": query
    }
sort_aggregation = {
        "$sort": { 
            "metadata.datetime": pymongo.DESCENDING,
        }
    }
group_aggregation = { 
    "$group": { 
        "_id": { field: '${}'.format(key) for field, key in parameter_dict.items() },
        "degeneracy": {"$sum": 1}, # number matching data sets
        "latest":     {"$first": "$gfs_id"} # unique gridfs id of file
    }
}
second_sort_aggregation = {
    "$sort": { 
        "_id.nmolecules": pymongo.DESCENDING,
    }
}


aggregation_pipeline = [ 
    match_aggregation, sort_aggregation, group_aggregation, second_sort_aggregation ]
cursor = fp.filepad.aggregate(aggregation_pipeline)

# res_list = []
for i, c in enumerate(cursor): 
    content, metadata = fp.get_file_by_id(c["latest"])
    nmolecules = int(c["_id"]["nmolecules"])
    data_str = io.StringIO(content.decode())
    data = np.loadtxt(data_str, comments='#')
    d = data[0] # distance bins
    rdf = data[1:]
    res_dict[nmolecules] = {'dist': d, 'rdf': rdf}
    # res_list.append(data)
    print('.',end='')
print('')

............

n = len(res_dict)
cols = 2 if n > 1 else 1
rows = round(n/cols)
cutoff_index_inner = 1
cutoff_index_outer = -1
if rows > 1:
    positions = [(i,j) for i in range(rows) for j in range(cols)][:n]
else:
    positions = [i for i in range(cols)][:n]
    
fig, ax = plt.subplots(rows,cols,figsize=(5*cols,4*rows))
if not isinstance(ax, Iterable):
    ax = [ax]
# for key, grp in res_df.groupby(['nmolecules']):
for pos, (nmolecules, data) in zip(positions, res_dict.items()):
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][0][cutoff_index_inner:cutoff_index_outer], label='First frame RDF')
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][len(data)//2][cutoff_index_inner:cutoff_index_outer],label='Intermediate frame RDF')
    ax[pos].plot(data['dist'][cutoff_index_inner:cutoff_index_outer],data['rdf'][-1][cutoff_index_inner:cutoff_index_outer],label='Last frame RDF')
    ax[pos].set_title(nmolecules)
    ax[pos].legend()

fig.tight_layout()
# fig.legend()
fig.show()

	object_count	earliest	latest	project
0	1653	2020-07-21 01:13:12.200383	2020-07-21 01:13:13.756743	2020-07-21-passivation-trial
1	278	2020-07-20 22:19:46.798328	2020-07-20 22:19:48.273066	2020-07-20-passivation-trial-d
2	316	2020-07-20 16:28:44.921863	2020-07-20 16:28:47.699103	2020-07-20-passivation-trial-c
3	278	2020-07-20 12:31:37.328725	2020-07-20 12:31:41.060445	2020-07-20-passivation-trial-b
4	278	2020-07-20 10:48:46.162767	2020-07-20 10:48:47.754857	2020-07-20-passivation-trial
5	1674	2020-07-19 22:52:59.921460	2020-07-19 22:53:01.334582	2020-07-19-passivation
6	278	2020-07-19 00:39:08.874821	2020-07-19 00:39:09.172045	2020-07-19-passivation-trial-a
7	278	2020-07-19 00:24:15.492750	2020-07-19 00:24:15.791253	2020-07-18-passivation-trial-c
8	152	2020-07-18 22:33:35.817622	2020-07-18 22:33:36.072923	2020-07-18-passivation-trial-b
9	278	2020-07-18 17:24:37.304667	2020-07-18 17:24:37.643817	2020-07-18-passivation-trial
10	278	2020-07-18 15:13:57.808691	2020-07-18 15:13:58.077915	2020-08-18-passivation-trial
11	121	2020-07-17 21:44:13.197047	2020-07-17 21:44:13.508164	2020-07-17-passivation-trial
12	1386	2020-07-09 01:30:15.276530	2020-07-09 01:30:16.819138	2020-07-09-passivation
13	350	2020-07-08 13:31:27.106684	2020-07-08 14:02:41.994152	2020-07-08-passiv-trial-revisited
14	236	2020-07-02 19:37:19.035754	2020-07-02 19:37:19.770123	2020-07-03-passiv-trial-revisited
15	54	2020-07-02 03:49:00.103034	2020-07-02 03:49:00.325450	2020-07-01-passiv-trial-revisited
16	6	2020-05-11 15:34:17.973141	2020-05-11 15:34:18.054752	2020-05-11-2nd-passiv-trial
17	64	2020-05-11 00:46:35.427907	2020-05-11 00:46:35.637375	2020-05-11-passiv-trial
18	76	2020-05-09 23:23:58.405636	2020-05-09 23:23:58.629348	2020-05-10-passiv-trial
19	54	2020-05-09 18:06:29.244729	2020-05-09 18:06:29.470488	2020-05-09-parametric
20	276	2020-05-08 17:40:43.151560	2020-05-08 20:09:19.785964	2020-05-08-final
21	8	2020-05-08 16:46:54.125267	2020-05-08 16:46:55.061579	2020-05-08-dtool-trial
22	102	2020-05-06 19:57:34.090873	2020-05-06 23:02:33.696573	2020-05-06-indenter-passivation-trial
23	4	2020-05-04 19:56:27.458671	2020-05-04 19:56:27.458686	2020-05-04-gmx-em-dtool-trial
24	1	2020-05-04 17:47:46.398832	2020-05-04 17:47:46.398832	2020-04-29-gmx-nvt-trial
25	15	2020-04-29 20:03:40.694070	2020-04-29 21:43:41.499280	2020-04-23-gmx-nvt-trial
26	102	2020-04-23 00:42:50.738462	2020-04-23 12:29:04.927501	2020-04-23-indenter-passivation-trial
27	6	2020-04-22 23:52:50.724373	2020-04-22 23:52:50.724774	2020-04-22-gmx-nvt-trial
28	30	2020-04-22 20:12:52.975107	2020-04-22 20:12:52.992097	2020-04-22-intermediate-trial
29	12	2020-04-22 15:35:33.694291	2020-04-22 19:37:27.812809	2020-04-22-trajectory-rendering-trial
30	23	2020-04-22 00:22:22.239748	2020-04-22 00:22:22.241080	2020-04-21-gmx-chain-wf-trial
31	2	2020-04-21 21:08:51.961011	2020-04-21 21:08:51.961024	2020-04-21-gmx-solvate-trial
32	42	2020-04-21 17:33:16.919815	2020-04-21 23:19:49.893920	2020-04-21-intermediate-trial
33	7	2020-04-21 15:35:02.223005	2020-04-21 15:35:02.223026	2020-04-21-gmx-pull-trial
34	15	2020-04-15 12:12:58.569894	2020-04-21 14:39:06.617272	2020-04-15-gmx-pull-prep-trial
35	74	2020-04-15 01:58:21.918222	2020-04-21 13:38:43.462681	2020-04-15-intermediate-trial
36	4	2020-04-15 00:28:04.783910	2020-04-15 00:28:04.783995	2020-04-15-gmx-em-trial
37	3	2020-04-14 21:19:31.298223	2020-04-14 21:19:31.298232	2020-04-14-gmx-prep-trial
38	2	2020-04-14 17:14:15.144625	2020-04-14 20:23:20.587799	2020-04-14-packmol-trial
39	2	2020-04-03 01:25:09.142195	2020-04-07 16:18:42.232568	2020-04-02-surfactant-molecule-measures-trial
40	5	2020-04-02 21:18:13.804918	2020-04-12 20:56:30.319559	2020-03-31-local-trial
41	39	2020-03-25 12:11:11.906332	2020-03-25 22:47:26.604903	juwels-pull-2020-03-17
42	8	2020-03-13 12:33:31.922163	2020-03-13 12:33:31.922497	juwels-afm-probe-solvation-trial-a-2020-03-13
43	10	2020-03-12 12:31:29.329732	2020-03-12 15:55:54.013161	juwels-gromacs-em-2020-03-12
44	42	2020-03-11 18:45:59.775161	2020-03-12 00:15:43.047626	juwels-gromacs-prep-2020-03-11
45	15	2020-03-09 18:13:09.455387	2020-03-09 23:30:45.006483	juwels-packmol-2020-03-09

	type	step	name	earliest	latest	object_count
0	mp4_file	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403569	2020-07-21 01:13:13.756743	12
1	surfactant_tail_rmsd	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403567	2020-07-21 01:13:13.756741	12
2	surfactant_head_rmsd	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403564	2020-07-21 01:13:13.756738	12
3	substrate_rmsd	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403561	2020-07-21 01:13:13.756736	12
4	counterion_rmsd	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403559	2020-07-21 01:13:13.756734	12
5	surfactant_tail_surfactant_tail_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403556	2020-07-21 01:13:13.756731	12
6	surfactant_head_surfactant_tail_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403554	2020-07-21 01:13:13.756729	12
7	surfactant_head_surfactant_head_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403551	2020-07-21 01:13:13.756727	12
8	substrate_surfactant_tail_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403549	2020-07-21 01:13:13.756725	12
9	substrate_surfactant_head_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403546	2020-07-21 01:13:13.756722	12
10	substrate_substrate_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403544	2020-07-21 01:13:13.756720	12
11	counterion_surfactant_tail_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403541	2020-07-21 01:13:13.756718	12
12	counterion_surfactant_head_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403539	2020-07-21 01:13:13.756716	12
13	counterion_substrate_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403536	2020-07-21 01:13:13.756713	12
14	counterion_counterion_rdf	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403534	2020-07-21 01:13:13.756711	12
15	index_file	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403531	2020-07-21 01:13:13.756709	12
16	topology_file	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403529	2020-07-21 01:13:13.756706	12
17	data_file	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403526	2020-07-21 01:13:13.756704	12
18	trajectory_file	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403524	2020-07-21 01:13:13.756702	12
19	energy_file	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403521	2020-07-21 01:13:13.756699	12
20	log_file	GromacsRelaxation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.403514	2020-07-21 01:13:13.756693	12
21	mp4_file	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391930	2020-07-21 01:13:13.743321	12
22	surfactant_tail_rmsd	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391927	2020-07-21 01:13:13.743318	12
23	surfactant_head_rmsd	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391925	2020-07-21 01:13:13.743316	12
24	substrate_rmsd	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391922	2020-07-21 01:13:13.743313	12
25	counterion_rmsd	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391920	2020-07-21 01:13:13.743311	12
26	surfactant_tail_surfactant_tail_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391917	2020-07-21 01:13:13.743309	12
27	surfactant_head_surfactant_tail_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391915	2020-07-21 01:13:13.743306	12
28	surfactant_head_surfactant_head_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391912	2020-07-21 01:13:13.743304	12
29	substrate_surfactant_tail_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391910	2020-07-21 01:13:13.743302	12
30	substrate_surfactant_head_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391908	2020-07-21 01:13:13.743299	12
31	substrate_substrate_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391905	2020-07-21 01:13:13.743297	12
32	counterion_surfactant_tail_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391903	2020-07-21 01:13:13.743295	12
33	counterion_surfactant_head_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391900	2020-07-21 01:13:13.743292	12
34	counterion_substrate_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391898	2020-07-21 01:13:13.743290	12
35	counterion_counterion_rdf	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391896	2020-07-21 01:13:13.743288	12
36	index_file	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391893	2020-07-21 01:13:13.743285	12
37	topology_file	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391891	2020-07-21 01:13:13.743283	12
38	data_file	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391888	2020-07-21 01:13:13.743280	12
39	trajectory_file	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391886	2020-07-21 01:13:13.743278	12
40	energy_file	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391883	2020-07-21 01:13:13.743275	12
41	log_file	GromacsNPTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.391877	2020-07-21 01:13:13.743269	12
42	mp4_file	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378962	2020-07-21 01:13:13.729390	12
43	surfactant_tail_rmsd	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378955	2020-07-21 01:13:13.729388	12
44	surfactant_head_rmsd	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378948	2020-07-21 01:13:13.729385	12
45	substrate_rmsd	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378942	2020-07-21 01:13:13.729383	12
46	counterion_rmsd	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378935	2020-07-21 01:13:13.729380	12
47	surfactant_tail_surfactant_tail_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378929	2020-07-21 01:13:13.729378	12
48	surfactant_head_surfactant_tail_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378922	2020-07-21 01:13:13.729376	13
49	surfactant_head_surfactant_head_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378915	2020-07-21 01:13:13.729373	13
50	substrate_surfactant_tail_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378909	2020-07-21 01:13:13.729371	13
51	substrate_surfactant_head_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378902	2020-07-21 01:13:13.729368	13
52	substrate_substrate_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378895	2020-07-21 01:13:13.729366	13
53	counterion_surfactant_tail_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378889	2020-07-21 01:13:13.729364	13
54	counterion_surfactant_head_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378882	2020-07-21 01:13:13.729361	13
55	counterion_substrate_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378875	2020-07-21 01:13:13.729359	13
56	counterion_counterion_rdf	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378868	2020-07-21 01:13:13.729356	13
57	index_file	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378861	2020-07-21 01:13:13.729354	13
58	topology_file	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378854	2020-07-21 01:13:13.729352	13
59	data_file	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378848	2020-07-21 01:13:13.729349	13
60	trajectory_file	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378841	2020-07-21 01:13:13.729347	13
61	energy_file	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378833	2020-07-21 01:13:13.729344	13
62	log_file	GromacsNVTEquilibration:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.378821	2020-07-21 01:13:13.729338	13
63	mp4_file	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365566	2020-07-21 01:13:13.714240	12
64	surfactant_tail_rmsd	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365564	2020-07-21 01:13:13.714238	12
65	surfactant_head_rmsd	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365561	2020-07-21 01:13:13.714235	12
66	substrate_rmsd	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365558	2020-07-21 01:13:13.714233	12
67	counterion_rmsd	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365556	2020-07-21 01:13:13.714230	12
68	surfactant_tail_surfactant_tail_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365554	2020-07-21 01:13:13.714228	12
69	surfactant_head_surfactant_tail_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365551	2020-07-21 01:13:13.714225	12
70	surfactant_head_surfactant_head_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365549	2020-07-21 01:13:13.714223	12
71	substrate_surfactant_tail_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365546	2020-07-21 01:13:13.714220	12
72	substrate_surfactant_head_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365544	2020-07-21 01:13:13.714218	12
73	substrate_substrate_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365542	2020-07-21 01:13:13.714215	12
74	counterion_surfactant_tail_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365539	2020-07-21 01:13:13.714213	12
75	counterion_surfactant_head_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365537	2020-07-21 01:13:13.714210	12
76	counterion_substrate_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365535	2020-07-21 01:13:13.714208	12
77	counterion_counterion_rdf	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365532	2020-07-21 01:13:13.714205	12
78	topology_file	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365530	2020-07-21 01:13:13.714203	12
79	data_file	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365527	2020-07-21 01:13:13.714200	12
80	trajectory_file	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365525	2020-07-21 01:13:13.714198	12
81	energy_file	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365522	2020-07-21 01:13:13.714194	12
82	log_file	GromacsEnergyMinimizationAfterSolvation:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.365516	2020-07-21 01:13:13.714189	12
83	topology_file	GromacsSolvate:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353316	2020-07-21 01:13:13.700739	12
84	data_file	GromacsSolvate:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353308	2020-07-21 01:13:13.700735	12
85	mp4_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353057	2020-07-21 01:13:13.700579	12
86	surfactant_tail_rmsd	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353054	2020-07-21 01:13:13.700576	12
87	surfactant_head_rmsd	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353052	2020-07-21 01:13:13.700574	12
88	substrate_rmsd	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353049	2020-07-21 01:13:13.700571	12
89	counterion_rmsd	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353047	2020-07-21 01:13:13.700569	12
90	surfactant_tail_surfactant_tail_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353044	2020-07-21 01:13:13.700567	12
91	surfactant_head_surfactant_tail_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353042	2020-07-21 01:13:13.700564	12
92	surfactant_head_surfactant_head_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353039	2020-07-21 01:13:13.700561	12
93	substrate_surfactant_tail_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353037	2020-07-21 01:13:13.700559	12
94	substrate_surfactant_head_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353034	2020-07-21 01:13:13.700556	12
95	substrate_substrate_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353032	2020-07-21 01:13:13.700554	12
96	counterion_surfactant_tail_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353029	2020-07-21 01:13:13.700551	12
97	counterion_surfactant_head_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353027	2020-07-21 01:13:13.700549	12
98	counterion_substrate_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353025	2020-07-21 01:13:13.700546	12
99	counterion_counterion_rdf	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353022	2020-07-21 01:13:13.700544	12
100	topology_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353020	2020-07-21 01:13:13.700541	12
101	pullx_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353017	2020-07-21 01:13:13.700539	12
102	pullf_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353015	2020-07-21 01:13:13.700536	12
103	data_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353012	2020-07-21 01:13:13.700534	12
104	compressed_trajectory_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353010	2020-07-21 01:13:13.700531	12
105	trajectory_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353007	2020-07-21 01:13:13.700529	12
106	energy_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.353004	2020-07-21 01:13:13.700526	12
107	log_file	GromacsPull:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.352999	2020-07-21 01:13:13.700520	12
108	input_file	GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.340217	2020-07-21 01:13:13.686957	12
109	index_file	GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.340215	2020-07-21 01:13:13.686954	12
110	topology_file	GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.340212	2020-07-21 01:13:13.686952	12
111	data_file	GromacsPullPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.340208	2020-07-21 01:13:13.686948	12
112	mp4_file	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339894	2020-07-21 01:13:13.686626	12
113	surfactant_tail_rmsd	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339892	2020-07-21 01:13:13.686624	12
114	surfactant_head_rmsd	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339889	2020-07-21 01:13:13.686621	12
115	substrate_rmsd	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339887	2020-07-21 01:13:13.686619	12
116	counterion_rmsd	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339884	2020-07-21 01:13:13.686617	12
117	surfactant_tail_surfactant_tail_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339882	2020-07-21 01:13:13.686614	12
118	surfactant_head_surfactant_tail_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339880	2020-07-21 01:13:13.686612	12
119	surfactant_head_surfactant_head_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339877	2020-07-21 01:13:13.686610	12
120	substrate_surfactant_tail_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339875	2020-07-21 01:13:13.686607	12
121	substrate_surfactant_head_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339873	2020-07-21 01:13:13.686605	12
122	substrate_substrate_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339870	2020-07-21 01:13:13.686603	12
123	counterion_surfactant_tail_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339868	2020-07-21 01:13:13.686601	12
124	counterion_surfactant_head_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339865	2020-07-21 01:13:13.686598	12
125	counterion_substrate_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339863	2020-07-21 01:13:13.686596	12
126	counterion_counterion_rdf	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339860	2020-07-21 01:13:13.686593	12
127	data_file	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339858	2020-07-21 01:13:13.686591	12
128	trajectory_file	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339855	2020-07-21 01:13:13.686589	12
129	energy_file	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339852	2020-07-21 01:13:13.686586	12
130	log_file	GromacsEnergyMinimization:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.339847	2020-07-21 01:13:13.686580	12
131	restraint_file	GromacsPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.327768	2020-07-21 01:13:13.671310	12
132	topology_file	GromacsPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.327765	2020-07-21 01:13:13.671303	12
133	data_file	GromacsPrep:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.327761	2020-07-21 01:13:13.671294	12
134	png_file	SphericalSurfactantPacking:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.327462	2020-07-21 01:13:13.670757	12
135	data_file	SphericalSurfactantPacking:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.327455	2020-07-21 01:13:13.670746	12
136	png_file	PackingConstraintSpheres:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.288523	2020-07-21 01:13:12.288523	1
137	indenter_file	PackingConstraintSpheres:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.288514	2020-07-21 01:13:12.288514	1
138	png_file	SurfactantMoleculeMeasures:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.254026	2020-07-21 01:13:12.254026	1
139	indenter_file	SurfactantMoleculeMeasures:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.254018	2020-07-21 01:13:12.254018	1
140	png_file	IndenterBoundingSphere:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.200392	2020-07-21 01:13:12.200392	1
141	indenter_file	IndenterBoundingSphere:ProcessAnalyzeAndVisualize:push_filepad	NaN	2020-07-21 01:13:12.200383	2020-07-21 01:13:12.200383	1
142	input	GromacsEnergyMinimization:GromacsTrajectoryVisualization:push_infiles	renumber_png.sh	None	None	1
143	input	GromacsEnergyMinimizationAfterSolvation:GromacsEnergyMinimizationAfterSolvationMain:push_infiles	em_solvated.mdp	None	None	1
144	input	GromacsEnergyMinimization:GromacsTrajectoryVisualization:push_infiles	movie.pml.template	None	None	1
145	input	GromacsEnergyMinimization:GromacsEnergyMinimizationMain:push_infiles	em.mdp	None	None	1
146	input	GromacsNVTEquilibration:GromacsNVTEquilibrationMain:push_infiles	nvt.mdp	None	None	1
147	surfactant_file	SurfactantMoleculeMeasures:SurfactantMoleculeMeasuresMain:push_infiles	1_SDS.pdb	None	None	1
148	indenter_file	IndenterBoundingSphere:IndenterBoundingSphereMain:push_infiles	AU_111_r_25.pdb	None	None	1
149	template	SphericalSurfactantPacking:push_infiles	sphere.inp.template	None	None	1
150	data	SphericalSurfactantPacking:push_infiles	1_NA.pdb	None	None	1
151	input	GromacsPullPrep:GromacsPullPrepMain:push_infiles	sys.top.template	None	None	1
152	input	GromacsNPTEquilibration:GromacsNPTEquilibrationMain:push_infiles	npt.mdp	None	None	1
153	input	GromacsRelaxation:GromacsRelaxationMain:push_infiles	relax.mdp	None	None	1
154	input	GromacsPullPrep:GromacsPullPrepMain:push_infiles	pull.mdp.template	None	None	1

	type	name	earliest	latest	object_count
0	mp4_file	NaN	2020-07-21 01:13:12.339894	2020-07-21 01:13:13.686626	12
1	surfactant_tail_rmsd	NaN	2020-07-21 01:13:12.339892	2020-07-21 01:13:13.686624	12
2	surfactant_head_rmsd	NaN	2020-07-21 01:13:12.339889	2020-07-21 01:13:13.686621	12
3	substrate_rmsd	NaN	2020-07-21 01:13:12.339887	2020-07-21 01:13:13.686619	12
4	counterion_rmsd	NaN	2020-07-21 01:13:12.339884	2020-07-21 01:13:13.686617	12
5	surfactant_tail_surfactant_tail_rdf	NaN	2020-07-21 01:13:12.339882	2020-07-21 01:13:13.686614	12
6	surfactant_head_surfactant_tail_rdf	NaN	2020-07-21 01:13:12.339880	2020-07-21 01:13:13.686612	12
7	surfactant_head_surfactant_head_rdf	NaN	2020-07-21 01:13:12.339877	2020-07-21 01:13:13.686610	12
8	substrate_surfactant_tail_rdf	NaN	2020-07-21 01:13:12.339875	2020-07-21 01:13:13.686607	12
9	substrate_surfactant_head_rdf	NaN	2020-07-21 01:13:12.339873	2020-07-21 01:13:13.686605	12
10	substrate_substrate_rdf	NaN	2020-07-21 01:13:12.339870	2020-07-21 01:13:13.686603	12
11	counterion_surfactant_tail_rdf	NaN	2020-07-21 01:13:12.339868	2020-07-21 01:13:13.686601	12
12	counterion_surfactant_head_rdf	NaN	2020-07-21 01:13:12.339865	2020-07-21 01:13:13.686598	12
13	counterion_substrate_rdf	NaN	2020-07-21 01:13:12.339863	2020-07-21 01:13:13.686596	12
14	counterion_counterion_rdf	NaN	2020-07-21 01:13:12.339860	2020-07-21 01:13:13.686593	12
15	data_file	NaN	2020-07-21 01:13:12.339858	2020-07-21 01:13:13.686591	12
16	trajectory_file	NaN	2020-07-21 01:13:12.339855	2020-07-21 01:13:13.686589	12
17	energy_file	NaN	2020-07-21 01:13:12.339852	2020-07-21 01:13:13.686586	12
18	log_file	NaN	2020-07-21 01:13:12.339847	2020-07-21 01:13:13.686580	12

	type	name	earliest	latest	object_count
0	mp4_file	NaN	2020-07-21 01:13:12.353057	2020-07-21 01:13:13.700579	12
1	surfactant_tail_rmsd	NaN	2020-07-21 01:13:12.353054	2020-07-21 01:13:13.700576	12
2	surfactant_head_rmsd	NaN	2020-07-21 01:13:12.353052	2020-07-21 01:13:13.700574	12
3	substrate_rmsd	NaN	2020-07-21 01:13:12.353049	2020-07-21 01:13:13.700571	12
4	counterion_rmsd	NaN	2020-07-21 01:13:12.353047	2020-07-21 01:13:13.700569	12
5	surfactant_tail_surfactant_tail_rdf	NaN	2020-07-21 01:13:12.353044	2020-07-21 01:13:13.700567	12
6	surfactant_head_surfactant_tail_rdf	NaN	2020-07-21 01:13:12.353042	2020-07-21 01:13:13.700564	12
7	surfactant_head_surfactant_head_rdf	NaN	2020-07-21 01:13:12.353039	2020-07-21 01:13:13.700561	12
8	substrate_surfactant_tail_rdf	NaN	2020-07-21 01:13:12.353037	2020-07-21 01:13:13.700559	12
9	substrate_surfactant_head_rdf	NaN	2020-07-21 01:13:12.353034	2020-07-21 01:13:13.700556	12
10	substrate_substrate_rdf	NaN	2020-07-21 01:13:12.353032	2020-07-21 01:13:13.700554	12
11	counterion_surfactant_tail_rdf	NaN	2020-07-21 01:13:12.353029	2020-07-21 01:13:13.700551	12
12	counterion_surfactant_head_rdf	NaN	2020-07-21 01:13:12.353027	2020-07-21 01:13:13.700549	12
13	counterion_substrate_rdf	NaN	2020-07-21 01:13:12.353025	2020-07-21 01:13:13.700546	12
14	counterion_counterion_rdf	NaN	2020-07-21 01:13:12.353022	2020-07-21 01:13:13.700544	12
15	topology_file	NaN	2020-07-21 01:13:12.353020	2020-07-21 01:13:13.700541	12
16	pullx_file	NaN	2020-07-21 01:13:12.353017	2020-07-21 01:13:13.700539	12
17	pullf_file	NaN	2020-07-21 01:13:12.353015	2020-07-21 01:13:13.700536	12
18	data_file	NaN	2020-07-21 01:13:12.353012	2020-07-21 01:13:13.700534	12
19	compressed_trajectory_file	NaN	2020-07-21 01:13:12.353010	2020-07-21 01:13:13.700531	12
20	trajectory_file	NaN	2020-07-21 01:13:12.353007	2020-07-21 01:13:13.700529	12
21	energy_file	NaN	2020-07-21 01:13:12.353004	2020-07-21 01:13:13.700526	12
22	log_file	NaN	2020-07-21 01:13:12.352999	2020-07-21 01:13:13.700520	12

		Restraint-Pot.	Position-Rest.	COM-Pull-En.	Potential	Kinetic-En.	Total-Energy	Temperature	Pressure	Constr.-rmsd
nmolecules	time
306	0.00	7031.586914	0.000000	0.016863	-671124.8750	58.650429	-671066.2500	0.523976	3025.780029	0.000002
	0.02	3245.086182	0.040132	41.408371	-674064.5625	2971.016113	-671093.5625	26.542702	2949.398438	0.000002
	0.04	889.867371	1.412355	268.745911	-675391.7500	4292.286621	-671099.4375	38.346775	2895.226074	0.000002
	0.06	389.765137	7.575518	415.137787	-675597.5625	4491.179199	-671106.3750	40.123657	2882.516602	0.000002
	0.08	300.085297	19.875889	393.292969	-675509.5000	4396.767578	-671112.7500	39.280193	2876.513672	0.000002
...	...	...	...	...	...	...	...	...	...	...
44	1.92	32.139790	715.891907	115.093407	-587792.1875	1695.226074	-586096.9375	105.396149	2990.895264	0.000002
	1.94	37.243038	714.876648	109.428764	-587826.5000	1733.902222	-586092.6250	107.800735	2987.830811	0.000002
	1.96	39.406776	720.091187	108.039062	-587847.4375	1759.293701	-586088.1250	109.379379	2990.757324	0.000002
	1.98	40.590767	726.882629	107.895409	-587891.2500	1807.384766	-586083.8750	112.369308	2998.117920	0.000002
	2.00	40.944344	731.684814	95.964859	-587859.8750	1781.397583	-586078.5000	110.753632	3003.758789	0.000003

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	...	Pres-YX	Pres-YY	Pres-YZ	Pres-ZX	Pres-ZY	Pres-ZZ	#Surf*SurfTen	T-rest
nmolecules	step
44	0.0	0.0	17870.773438	10515.197266	1631.395508	1011.865295	11690.691406	-583627.3750	-18877.916016	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	1.0	1.0	17581.853516	10477.472656	1631.442139	1008.043762	11688.764648	-583627.3750	-18878.417969	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	2.0	2.0	17238.019531	10432.553711	1631.510254	1003.449646	11686.456055	-583627.2500	-18879.031250	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	3.0	3.0	16829.621094	10379.159180	1631.608765	997.923401	11683.688477	-583627.1875	-18879.783203	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	4.0	4.0	16345.567383	10315.792969	1631.750732	991.267944	11680.367188	-583627.0625	-18880.710938	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
88	9995.0	9995.0	138.642334	7118.208008	2281.199951	517.900940	22626.222656	-582644.4375	-55745.808594	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	9996.0	9996.0	139.351089	7121.719727	2281.218262	517.639038	22625.919922	-582645.4375	-55747.671875	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	9997.0	9997.0	139.446945	7117.706543	2281.231689	517.956787	22626.304688	-582643.6875	-55747.363281	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	9999.0	9999.0	138.082321	7119.891113	2281.240723	517.767578	22626.089844	-582644.5000	-55748.457031	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
	10000.0	10000.0	140.254425	7117.438477	2281.290039	518.000366	22626.398438	-582642.5625	-55750.425781	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	nmolecule	0								...	524
	coord	1	1 ref	1 dX	1 dY	1 dZ	1 g 1 X	1 g 1 Y	1 g 1 Z	...	1 dY	1 dZ	1 g 1 X	1 g 1 Y	1 g 1 Z	1 g 2 X	1 g 2 Y	1 g 2 Z
nmolecules	time
525	0.0	3.04277	3.04264	-3.012120	-0.408986	0.135390	6.778	6.802	6.763	...	-0.989800	2.01098	6.778	6.802	6.763	4.71159	5.81220	8.77398
	0.2	3.02987	3.02264	-2.997960	-0.408835	0.158761	6.778	6.802	6.763	...	-0.953240	2.00705	6.778	6.802	6.763	4.72044	5.84876	8.77005
	0.4	3.01117	3.00264	-2.974230	-0.430042	0.190234	6.778	6.802	6.763	...	-0.975928	1.99767	6.778	6.802	6.763	4.74648	5.82607	8.76067
	0.6	2.97040	2.98264	-2.931880	-0.424803	0.216552	6.778	6.802	6.763	...	-0.958169	1.97888	6.778	6.802	6.763	4.76680	5.84383	8.74188
	0.8	2.95784	2.96264	-2.927250	-0.351821	0.237092	6.778	6.802	6.763	...	-0.957422	1.97881	6.778	6.802	6.763	4.79279	5.84458	8.74181
	1.0	2.93029	2.94264	-2.903060	-0.248654	0.311457	6.778	6.802	6.763	...	-0.939117	1.96459	6.778	6.802	6.763	4.80662	5.86288	8.72759
	1.2	2.91763	2.92264	-2.895960	-0.235826	0.265267	6.778	6.802	6.763	...	-0.951476	1.94226	6.778	6.802	6.763	4.82482	5.85052	8.70526
	1.4	2.91436	2.90264	-2.892570	-0.184162	0.304320	6.778	6.802	6.763	...	-0.934430	1.96228	6.778	6.802	6.763	4.84116	5.86757	8.72528
	1.6	2.89784	2.88264	-2.880380	-0.148715	0.280632	6.778	6.802	6.763	...	-0.899018	1.96046	6.778	6.802	6.763	4.84962	5.90298	8.72346
	1.8	2.86755	2.86264	-2.849370	-0.157306	0.281413	6.778	6.802	6.763	...	-0.868220	1.98504	6.778	6.802	6.763	4.89189	5.93378	8.74804
	2.0	2.85090	2.84264	-2.839810	-0.153754	0.198643	6.778	6.802	6.763	...	-0.848802	1.98689	6.778	6.802	6.763	4.91739	5.95320	8.74989
394	0.0	3.05858	3.05857	1.789350	2.047640	1.400090	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	3.04088	3.03857	1.779700	2.034340	1.393240	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	3.02171	3.01857	1.768700	2.027730	1.375040	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	3.00259	2.99857	1.740630	2.033640	1.360160	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.97199	2.97857	1.720970	2.024300	1.331620	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.94701	2.95857	1.692370	2.013770	1.328710	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.93167	2.93857	1.673210	2.016410	1.314980	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.91156	2.91857	1.653800	1.972770	1.360270	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.91143	2.89857	1.675850	1.939350	1.380890	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.89987	2.87857	1.662630	1.938180	1.374190	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.85929	2.85857	1.657670	1.902710	1.344380	6.749	6.708	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
438	0.0	3.06981	3.06970	-1.695070	-0.217678	2.550120	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	3.05348	3.04970	-1.693840	-0.206201	2.532210	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	3.03029	3.02970	-1.679390	-0.212995	2.513350	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	3.01049	3.00970	-1.651080	-0.232905	2.506540	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.98448	2.98970	-1.625860	-0.243347	2.490880	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.97258	2.96970	-1.629800	-0.202889	2.477660	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.95605	2.94970	-1.645190	-0.176932	2.449540	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.93565	2.92970	-1.609450	-0.180835	2.448470	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.92409	2.90970	-1.584400	-0.207038	2.448900	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.88338	2.88970	-1.591590	-0.226784	2.393590	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.88183	2.86970	-1.582800	-0.263782	2.393770	6.789	6.821	6.695	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
350	0.0	2.91510	2.91528	1.507870	-1.258800	-2.153960	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	2.90488	2.89528	1.529000	-1.219100	-2.148080	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	2.84159	2.87528	1.530890	-1.155010	-2.096890	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	2.81071	2.85528	1.512660	-1.133550	-2.080150	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.80978	2.83528	1.507760	-1.118860	-2.090380	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.78623	2.81528	1.499580	-1.095290	-2.077190	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.81164	2.79528	1.508110	-1.089030	-2.108300	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.79608	2.77528	1.510560	-1.114200	-2.072390	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.77875	2.75528	1.514690	-1.105710	-2.050500	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.74687	2.73528	1.542850	-1.104440	-1.986230	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.74250	2.71528	1.554230	-1.109970	-1.968160	6.720	6.803	6.789	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
263	0.0	3.09102	3.09096	1.908980	-1.902280	1.513770	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	3.08303	3.07096	1.897440	-1.899630	1.515330	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	3.06393	3.05096	1.884530	-1.895090	1.498270	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	3.03792	3.03096	1.861010	-1.898400	1.470270	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	3.00997	3.01096	1.835840	-1.871290	1.479160	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	3.01847	2.99096	1.830880	-1.825480	1.557770	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.99438	2.97096	1.814530	-1.797550	1.562890	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.94768	2.95096	1.776810	-1.786610	1.529630	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.93524	2.93096	1.777020	-1.753860	1.543310	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.93640	2.91096	1.773920	-1.746710	1.557130	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.89080	2.89096	1.741280	-1.774530	1.475040	6.719	6.928	6.938	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
175	0.0	2.98740	2.98736	-1.122020	2.768040	-0.059902	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	2.97408	2.96736	-1.123540	2.753070	-0.058330	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	2.95666	2.94736	-1.117920	2.736390	-0.065259	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	2.94137	2.92736	-1.130220	2.714980	-0.056259	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.91696	2.90736	-1.142280	2.683790	-0.033462	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.89288	2.88736	-1.123830	2.665650	-0.008771	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.87267	2.86736	-1.129610	2.641120	0.026109	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.84310	2.84736	-1.133510	2.606840	0.052546	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.80902	2.82736	-1.112690	2.578840	0.045894	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.79277	2.80736	-1.078440	2.574780	0.083585	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.78704	2.78736	-1.093620	2.562340	0.077222	6.715	6.790	6.825	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
131	0.0	3.09045	3.09024	-0.850902	-1.544340	2.538080	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	3.07859	3.07024	-0.839331	-1.542740	2.528470	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	3.06101	3.05024	-0.847569	-1.536360	2.508190	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	3.03237	3.03024	-0.851869	-1.515500	2.484520	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	3.00622	3.01024	-0.876325	-1.489750	2.459690	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.99657	2.99024	-0.872745	-1.444920	2.475870	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.98199	2.97024	-0.872804	-1.391780	2.488660	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.95146	2.95024	-0.910132	-1.348670	2.462490	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.93310	2.93024	-0.985504	-1.304640	2.435110	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.89833	2.91024	-1.065910	-1.269290	2.377610	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.90315	2.89024	-1.210220	-1.294830	2.299370	6.785	6.624	6.675	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
44	0.0	2.87939	2.87932	2.060960	1.771370	-0.951617	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	2.86783	2.85932	2.040840	1.776020	-0.951409	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	2.85606	2.83932	2.051480	1.746500	-0.947749	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	2.85435	2.81932	2.087230	1.707210	-0.936068	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.83880	2.79932	2.085300	1.679200	-0.943723	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.82847	2.77932	2.072130	1.663470	-0.969229	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.80850	2.75932	2.035380	1.663100	-0.989450	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.78703	2.73932	1.993030	1.647290	-1.040100	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.78783	2.71932	1.982900	1.656520	-1.046910	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.76353	2.69932	1.962760	1.611780	-1.089430	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.73019	2.67932	1.842040	1.650360	-1.156340	6.660	6.627	6.730	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
306	0.0	2.99688	2.99656	2.234560	-1.989880	0.168659	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	2.97925	2.97656	2.211300	-1.988990	0.173106	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	2.95516	2.95656	2.200380	-1.964280	0.181320	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	2.92743	2.93656	2.182050	-1.938710	0.223479	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.89162	2.91656	2.168720	-1.900190	0.217636	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.86808	2.89656	2.178040	-1.854240	0.209316	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.87530	2.87656	2.253140	-1.777560	0.176006	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.86280	2.85656	2.276050	-1.725690	0.192927	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.83452	2.83656	2.271610	-1.677400	0.246300	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.82773	2.81656	2.250710	-1.695120	0.238560	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.81095	2.79656	2.243790	-1.667140	0.295791	6.785	6.759	6.790	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
88	0.0	2.96425	2.96426	2.212950	0.236086	-1.958030	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	2.92867	2.94426	2.223590	0.251374	-1.889320	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	2.92826	2.92426	2.242730	0.263759	-1.864220	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	2.91728	2.90426	2.234980	0.290677	-1.852260	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.88076	2.88426	2.200240	0.262680	-1.840850	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.83341	2.86426	2.139580	0.235171	-1.842590	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.81403	2.84426	2.115710	0.215252	-1.842880	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.80890	2.82426	2.089990	0.163857	-1.869500	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.79677	2.80426	2.004410	0.190867	-1.941090	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.77417	2.78426	1.916760	0.124871	-2.001610	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.75556	2.76426	1.838440	0.051715	-2.051960	6.624	6.903	6.676	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
481	0.0	3.02906	3.02901	2.669190	0.972790	-1.050880	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	3.01282	3.00901	2.652500	0.961100	-1.057180	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	2.99702	2.98901	2.629840	0.947392	-1.080960	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	2.97670	2.96901	2.613230	0.903125	-1.102790	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	2.94319	2.94901	2.609950	0.822523	-1.083490	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.93229	2.92901	2.626970	0.805054	-1.024320	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.92167	2.90901	2.641930	0.794171	-0.962114	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.89589	2.88901	2.627770	0.756169	-0.953522	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.88875	2.86901	2.606340	0.751238	-0.993734	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.83390	2.84901	2.567070	0.659165	-1.003290	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.83436	2.82901	2.553330	0.675940	-1.028210	6.799	6.864	6.719	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
219	0.0	3.08224	3.08202	-1.097430	-2.056400	2.016690	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.2	3.07527	3.06202	-1.113480	-2.045040	2.008790	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.4	3.05837	3.04202	-1.153960	-2.016810	1.988590	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.6	3.02829	3.02202	-1.125190	-2.005830	1.970060	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	0.8	3.00249	3.00202	-1.052950	-2.017270	1.958800	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.0	2.98880	2.98202	-1.036230	-2.018110	1.945870	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.2	2.97650	2.96202	-1.045430	-2.050940	1.886870	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.4	2.94793	2.94202	-1.083810	-2.055260	1.814260	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.6	2.95220	2.92202	-1.108670	-2.032210	1.832060	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	1.8	2.91947	2.90202	-1.083090	-2.029880	1.797170	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2.0	2.88906	2.88202	-1.045460	-2.001590	1.802030	6.783	6.773	6.687	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

coord	nmolecules	time	1	1 dX	1 dY	1 dZ	1 g 1 X	1 g 1 Y	1 g 1 Z	1 g 2 X	1 g 2 Y	1 g 2 Z	1 ref
0	525	0.0	3.034922	0.010765	-0.024874	0.038641	6.778	6.802	6.763	6.788769	6.777123	6.801643	3.034867
1	525	0.2	3.018362	0.009615	-0.024132	0.039875	6.778	6.802	6.763	6.787619	6.777865	6.802876	3.014867
2	525	0.4	3.000280	0.009741	-0.021807	0.040659	6.778	6.802	6.763	6.787745	6.780190	6.803660	2.994867
3	525	0.6	2.975453	0.010267	-0.019831	0.041684	6.778	6.802	6.763	6.788271	6.782166	6.804685	2.974867
4	525	0.8	2.950287	0.008752	-0.017311	0.042727	6.778	6.802	6.763	6.786756	6.784685	6.805728	2.954867
5	525	1.0	2.931836	0.008292	-0.014080	0.044248	6.778	6.802	6.763	6.786296	6.787917	6.807249	2.934867
6	525	1.2	2.915855	0.006768	-0.010449	0.046073	6.778	6.802	6.763	6.784772	6.791548	6.809074	2.914867
7	525	1.4	2.900949	0.007208	-0.006410	0.047046	6.778	6.802	6.763	6.785213	6.795586	6.810047	2.894867
8	525	1.6	2.882969	0.007942	-0.004268	0.048136	6.778	6.802	6.763	6.785947	6.797730	6.811138	2.874867
9	525	1.8	2.863472	0.007217	-0.003854	0.050202	6.778	6.802	6.763	6.785222	6.798142	6.813203	2.854867
10	525	2.0	2.842942	0.006279	-0.004383	0.050293	6.778	6.802	6.763	6.784284	6.797614	6.813294	2.834867
11	394	0.0	3.039270	0.042538	0.071386	0.102983	6.749	6.708	6.695	6.791542	6.779382	6.797985	3.039219
12	394	0.2	3.021789	0.041296	0.071312	0.100680	6.749	6.708	6.695	6.790301	6.779309	6.795681	3.019219
13	394	0.4	3.005356	0.039927	0.070586	0.098463	6.749	6.708	6.695	6.788931	6.778582	6.793464	2.999219
14	394	0.6	2.981034	0.037926	0.066785	0.099624	6.749	6.708	6.695	6.786931	6.774782	6.794624	2.979219
15	394	0.8	2.955050	0.035288	0.063702	0.097570	6.749	6.708	6.695	6.784293	6.771699	6.792571	2.959219
16	394	1.0	2.934622	0.031641	0.064001	0.099887	6.749	6.708	6.695	6.780645	6.771998	6.794888	2.939219
17	394	1.2	2.917793	0.030269	0.064800	0.101330	6.749	6.708	6.695	6.779274	6.772797	6.796331	2.919219
18	394	1.4	2.902097	0.029058	0.061974	0.103267	6.749	6.708	6.695	6.778063	6.769971	6.798268	2.899219
19	394	1.6	2.884613	0.027490	0.059746	0.105912	6.749	6.708	6.695	6.776495	6.767743	6.800913	2.879219
20	394	1.8	2.865220	0.026572	0.058102	0.103044	6.749	6.708	6.695	6.775577	6.766099	6.798045	2.859219
21	394	2.0	2.845958	0.027230	0.059402	0.102275	6.749	6.708	6.695	6.776234	6.767399	6.797276	2.839219
22	438	0.0	3.034992	-0.005472	-0.046267	0.093900	6.789	6.821	6.695	6.783532	6.774730	6.788901	3.034932
23	438	0.2	3.018102	-0.005551	-0.046558	0.094535	6.789	6.821	6.695	6.783453	6.774439	6.789535	3.014932
24	438	0.4	3.000249	-0.005531	-0.045098	0.095311	6.789	6.821	6.695	6.783474	6.775899	6.790313	2.994932
25	438	0.6	2.975846	-0.004704	-0.042169	0.094428	6.789	6.821	6.695	6.784301	6.778829	6.789429	2.974932
26	438	0.8	2.951186	-0.005260	-0.039267	0.093976	6.789	6.821	6.695	6.783744	6.781730	6.788978	2.954932
27	438	1.0	2.931786	-0.005480	-0.035514	0.095374	6.789	6.821	6.695	6.783524	6.785483	6.790375	2.934932
28	438	1.2	2.915978	-0.002793	-0.034526	0.095118	6.789	6.821	6.695	6.786212	6.786471	6.790119	2.914932
29	438	1.4	2.899683	-0.001023	-0.032910	0.097669	6.789	6.821	6.695	6.787981	6.788087	6.792670	2.894932
30	438	1.6	2.881104	0.000461	-0.031330	0.098089	6.789	6.821	6.695	6.789465	6.789667	6.793090	2.874932
31	438	1.8	2.861946	-0.001186	-0.031247	0.098028	6.789	6.821	6.695	6.787818	6.789750	6.793029	2.854932
32	438	2.0	2.841929	-0.001993	-0.033535	0.100370	6.789	6.821	6.695	6.787011	6.787462	6.795371	2.834932
33	350	0.0	3.036760	0.080290	-0.023688	0.004388	6.720	6.803	6.789	6.800295	6.779309	6.793390	3.036711
34	350	0.2	3.018600	0.081297	-0.021077	0.003408	6.720	6.803	6.789	6.801301	6.781920	6.792409	3.016711
35	350	0.4	3.002237	0.082215	-0.019325	0.002584	6.720	6.803	6.789	6.802220	6.783672	6.791586	2.996711
36	350	0.6	2.977871	0.081499	-0.020808	0.001862	6.720	6.803	6.789	6.801503	6.782189	6.790863	2.976711
37	350	0.8	2.952839	0.081155	-0.021246	0.003388	6.720	6.803	6.789	6.801159	6.781751	6.792389	2.956711
38	350	1.0	2.931092	0.079251	-0.019686	0.007140	6.720	6.803	6.789	6.799255	6.783311	6.796141	2.936711
39	350	1.2	2.913704	0.079847	-0.018476	0.008928	6.720	6.803	6.789	6.799852	6.784521	6.797930	2.916711
40	350	1.4	2.898667	0.081984	-0.018416	0.012155	6.720	6.803	6.789	6.801989	6.784581	6.801156	2.896711
41	350	1.6	2.880808	0.084175	-0.016798	0.016169	6.720	6.803	6.789	6.804179	6.786199	6.805170	2.876711
42	350	1.8	2.861538	0.084581	-0.015834	0.019831	6.720	6.803	6.789	6.804585	6.787163	6.808832	2.856711
43	350	2.0	2.841458	0.081435	-0.017207	0.023532	6.720	6.803	6.789	6.801439	6.785790	6.812533	2.836711
44	263	0.0	3.037367	0.090980	-0.167417	-0.159162	6.719	6.928	6.938	6.809985	6.760580	6.778839	3.037313
45	263	0.2	3.019191	0.090540	-0.165881	-0.158784	6.719	6.928	6.938	6.809544	6.762115	6.779217	3.017313
46	263	0.4	3.003904	0.088006	-0.163625	-0.158928	6.719	6.928	6.938	6.807010	6.764372	6.779073	2.997313
47	263	0.6	2.979121	0.085891	-0.161594	-0.158608	6.719	6.928	6.938	6.804895	6.766403	6.779393	2.977313
48	263	0.8	2.953302	0.085188	-0.160774	-0.155233	6.719	6.928	6.938	6.804192	6.767223	6.782768	2.957313
49	263	1.0	2.931889	0.085824	-0.159726	-0.152970	6.719	6.928	6.938	6.804828	6.768271	6.785031	2.937313
50	263	1.2	2.914128	0.082727	-0.161103	-0.152828	6.719	6.928	6.938	6.801732	6.766893	6.785174	2.917313
51	263	1.4	2.897885	0.084754	-0.159780	-0.148310	6.719	6.928	6.938	6.803759	6.768217	6.789691	2.897313
52	263	1.6	2.881493	0.084530	-0.160834	-0.141476	6.719	6.928	6.938	6.803534	6.767163	6.796525	2.877313
53	263	1.8	2.861136	0.082015	-0.161506	-0.138388	6.719	6.928	6.938	6.801020	6.766491	6.799612	2.857313
54	263	2.0	2.842035	0.077943	-0.163131	-0.130951	6.719	6.928	6.938	6.796948	6.764866	6.807050	2.837313
55	175	0.0	3.030009	0.080984	-0.018843	-0.056047	6.715	6.790	6.825	6.795988	6.771153	6.768955	3.029957
56	175	0.2	3.012094	0.078401	-0.018740	-0.058484	6.715	6.790	6.825	6.793405	6.771258	6.766517	3.009957
57	175	0.4	2.997073	0.076897	-0.015878	-0.059774	6.715	6.790	6.825	6.791901	6.774119	6.765227	2.989957
58	175	0.6	2.972110	0.075941	-0.011430	-0.056586	6.715	6.790	6.825	6.790945	6.778567	6.768416	2.969957
59	175	0.8	2.946350	0.076670	-0.002939	-0.054902	6.715	6.790	6.825	6.791674	6.787057	6.770098	2.949957
60	175	1.0	2.924743	0.078043	0.005130	-0.055743	6.715	6.790	6.825	6.793047	6.795127	6.769258	2.929957
61	175	1.2	2.907656	0.079557	0.009705	-0.057966	6.715	6.790	6.825	6.794562	6.799702	6.767035	2.909957
62	175	1.4	2.889336	0.080505	0.011842	-0.056512	6.715	6.790	6.825	6.795509	6.801839	6.768489	2.889957
63	175	1.6	2.872079	0.080764	0.012456	-0.053796	6.715	6.790	6.825	6.795769	6.802453	6.771206	2.869957
64	175	1.8	2.853288	0.080410	0.016686	-0.051414	6.715	6.790	6.825	6.795415	6.806683	6.773587	2.849957
65	175	2.0	2.834097	0.079160	0.022666	-0.046314	6.715	6.790	6.825	6.794164	6.812662	6.778688	2.829957
66	131	0.0	3.037290	0.008238	0.222722	0.160375	6.785	6.624	6.675	6.793242	6.846719	6.835376	3.037226
67	131	0.2	3.017884	0.011374	0.218104	0.160173	6.785	6.624	6.675	6.796378	6.842101	6.835175	3.017226
68	131	0.4	3.004250	0.011501	0.219179	0.157313	6.785	6.624	6.675	6.796505	6.843176	6.832314	2.997226
69	131	0.6	2.980649	0.012166	0.217925	0.148020	6.785	6.624	6.675	6.797171	6.841922	6.823021	2.977226
70	131	0.8	2.956502	0.014619	0.211890	0.142922	6.785	6.624	6.675	6.799624	6.835887	6.817923	2.957226
71	131	1.0	2.934341	0.011296	0.210212	0.144027	6.785	6.624	6.675	6.796300	6.834209	6.819028	2.937226
72	131	1.2	2.916920	0.006992	0.209758	0.150397	6.785	6.624	6.675	6.791997	6.833754	6.825398	2.917226
73	131	1.4	2.898377	-0.001679	0.203074	0.153058	6.785	6.624	6.675	6.783325	6.827071	6.828059	2.897226
74	131	1.6	2.875767	-0.011129	0.199670	0.158375	6.785	6.624	6.675	6.773875	6.823667	6.833376	2.877226
75	131	1.8	2.858852	-0.014816	0.194977	0.165034	6.785	6.624	6.675	6.770189	6.818974	6.840034	2.857226
76	131	2.0	2.842520	-0.017206	0.186697	0.171387	6.785	6.624	6.675	6.767799	6.810694	6.846389	2.837226
77	44	0.0	3.020985	0.235488	0.313048	0.051773	6.660	6.627	6.730	6.895492	6.940044	6.781774	3.020933
78	44	0.2	3.001215	0.234083	0.311769	0.049222	6.660	6.627	6.730	6.894088	6.938766	6.779223	3.000932
79	44	0.4	2.986466	0.236713	0.311665	0.049282	6.660	6.627	6.730	6.896717	6.938662	6.779282	2.980932
80	44	0.6	2.965443	0.241006	0.310308	0.051667	6.660	6.627	6.730	6.901011	6.937304	6.781668	2.960932
81	44	0.8	2.943091	0.244133	0.312242	0.055588	6.660	6.627	6.730	6.904137	6.939239	6.785590	2.940933
82	44	1.0	2.919757	0.240311	0.313154	0.058092	6.660	6.627	6.730	6.900316	6.940152	6.788093	2.920932
83	44	1.2	2.897124	0.229952	0.315481	0.056023	6.660	6.627	6.730	6.889956	6.942479	6.786024	2.900933
84	44	1.4	2.879173	0.219895	0.314174	0.053120	6.660	6.627	6.730	6.879899	6.941172	6.783121	2.880933
85	44	1.6	2.864009	0.218257	0.311913	0.055171	6.660	6.627	6.730	6.878262	6.938910	6.785172	2.860933
86	44	1.8	2.845171	0.221283	0.306526	0.052747	6.660	6.627	6.730	6.881287	6.933523	6.782748	2.840933
87	44	2.0	2.825302	0.215318	0.307488	0.041060	6.660	6.627	6.730	6.875323	6.934485	6.771061	2.820933
88	306	0.0	3.030811	-0.003623	0.015265	0.000827	6.785	6.759	6.790	6.781381	6.774262	6.790828	3.030759
89	306	0.2	3.011870	-0.003788	0.013854	-0.000054	6.785	6.759	6.790	6.781216	6.772852	6.789947	3.010759
90	306	0.4	2.997445	-0.001588	0.014145	-0.001017	6.785	6.759	6.790	6.783416	6.773142	6.788983	2.990759
91	306	0.6	2.971440	0.000782	0.015051	-0.002138	6.785	6.759	6.790	6.785786	6.774048	6.787864	2.970759
92	306	0.8	2.946581	0.005614	0.016238	-0.003716	6.785	6.759	6.790	6.790618	6.775235	6.786285	2.950759
93	306	1.0	2.927147	0.007186	0.019300	-0.005143	6.785	6.759	6.790	6.792189	6.778297	6.784858	2.930759
94	306	1.2	2.909072	0.009374	0.018696	-0.006815	6.785	6.759	6.790	6.794379	6.777693	6.783186	2.910759
95	306	1.4	2.891492	0.009350	0.016756	-0.010996	6.785	6.759	6.790	6.794355	6.775753	6.779005	2.890759
96	306	1.6	2.873910	0.013068	0.014708	-0.013598	6.785	6.759	6.790	6.798072	6.773705	6.776403	2.870759
97	306	1.8	2.855294	0.014694	0.012045	-0.013692	6.785	6.759	6.790	6.799698	6.771042	6.776309	2.850759
98	306	2.0	2.836348	0.011107	0.012934	-0.014228	6.785	6.759	6.790	6.796111	6.771932	6.775773	2.830759
99	88	0.0	3.036086	0.236927	-0.256623	0.157635	6.624	6.903	6.676	6.860931	6.646373	6.833636	3.036024
100	88	0.2	3.016645	0.236229	-0.255601	0.160637	6.624	6.903	6.676	6.860234	6.647396	6.836638	3.016024
101	88	0.4	3.002765	0.232964	-0.252690	0.160751	6.624	6.903	6.676	6.856968	6.650306	6.836752	2.996024
102	88	0.6	2.979860	0.224186	-0.253779	0.152404	6.624	6.903	6.676	6.848190	6.649218	6.828404	2.976024
103	88	0.8	2.952067	0.217245	-0.251586	0.138371	6.624	6.903	6.676	6.841249	6.651411	6.814372	2.956024
104	88	1.0	2.931130	0.212298	-0.246777	0.130433	6.624	6.903	6.676	6.836303	6.656220	6.806434	2.936024
105	88	1.2	2.911752	0.205931	-0.241666	0.122064	6.624	6.903	6.676	6.829936	6.661331	6.798065	2.916024
106	88	1.4	2.893783	0.200155	-0.243981	0.111793	6.624	6.903	6.676	6.824159	6.659016	6.787794	2.896024
107	88	1.6	2.875490	0.198388	-0.244446	0.103339	6.624	6.903	6.676	6.822393	6.658551	6.779341	2.876024
108	88	1.8	2.856435	0.196086	-0.247459	0.090633	6.624	6.903	6.676	6.820091	6.655538	6.766634	2.856024
109	88	2.0	2.837190	0.192131	-0.250788	0.086682	6.624	6.903	6.676	6.816135	6.652208	6.762682	2.836024
110	481	0.0	3.034603	-0.014782	-0.072324	0.067737	6.799	6.864	6.719	6.784223	6.791673	6.786737	3.034543
111	481	0.2	3.018015	-0.014357	-0.072601	0.067114	6.799	6.864	6.719	6.784647	6.791395	6.786114	3.014543
112	481	0.4	3.000231	-0.014021	-0.072274	0.067635	6.799	6.864	6.719	6.784983	6.791722	6.786636	2.994543
113	481	0.6	2.974872	-0.015174	-0.070603	0.068136	6.799	6.864	6.719	6.783830	6.793394	6.787137	2.974543
114	481	0.8	2.950800	-0.015300	-0.069087	0.070114	6.799	6.864	6.719	6.783704	6.794910	6.789115	2.954543
115	481	1.0	2.931433	-0.014866	-0.067311	0.071437	6.799	6.864	6.719	6.784139	6.796686	6.790438	2.934543
116	481	1.2	2.915312	-0.016355	-0.063953	0.071661	6.799	6.864	6.719	6.782649	6.800044	6.790662	2.914543
117	481	1.4	2.898523	-0.017177	-0.062616	0.071685	6.799	6.864	6.719	6.781828	6.801380	6.790687	2.894543
118	481	1.6	2.880843	-0.019072	-0.062704	0.071277	6.799	6.864	6.719	6.779932	6.801293	6.790278	2.874543
119	481	1.8	2.862234	-0.019518	-0.062286	0.071343	6.799	6.864	6.719	6.779486	6.801711	6.790344	2.854543
120	481	2.0	2.842636	-0.018782	-0.062664	0.070667	6.799	6.864	6.719	6.780222	6.801333	6.789668	2.834543
121	219	0.0	3.035669	-0.001536	0.017633	0.134667	6.783	6.773	6.687	6.781467	6.790630	6.821668	3.035626
122	219	0.2	3.016866	-0.002555	0.017841	0.134384	6.783	6.773	6.687	6.780449	6.790838	6.821385	3.015626
123	219	0.4	3.003203	-0.003735	0.018608	0.134001	6.783	6.773	6.687	6.779269	6.791605	6.821002	2.995626
124	219	0.6	2.978078	-0.001805	0.016775	0.129465	6.783	6.773	6.687	6.781199	6.789773	6.816466	2.975626
125	219	0.8	2.951516	-0.001210	0.017160	0.121625	6.783	6.773	6.687	6.781794	6.790156	6.808625	2.955626
126	219	1.0	2.928783	-0.001437	0.016281	0.115233	6.783	6.773	6.687	6.781567	6.789278	6.802233	2.935626
127	219	1.2	2.912858	-0.004232	0.018190	0.111485	6.783	6.773	6.687	6.778772	6.791187	6.798486	2.915626
128	219	1.4	2.896929	-0.005499	0.018065	0.106649	6.783	6.773	6.687	6.777505	6.791062	6.793650	2.895626
129	219	1.6	2.880101	-0.002200	0.019840	0.099829	6.783	6.773	6.687	6.780804	6.792837	6.786830	2.875626
130	219	1.8	2.861178	0.000377	0.018248	0.094078	6.783	6.773	6.687	6.783382	6.791244	6.781079	2.855626
131	219	2.0	2.840425	0.006052	0.014156	0.090879	6.783	6.773	6.687	6.789057	6.787153	6.777880	2.835626

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	...	Pres-YX	Pres-YY	Pres-YZ	Pres-ZX	Pres-ZY	Pres-ZZ	#Surf*SurfTen	T-rest
nmolecules	step
350	0.0	0.0	106.049530	30969.919922	11162.725586	2396.903564	89897.656250	836729.812500	-3727134.50	...	57.025841	-273.003815	-23.946796	71.850533	-23.946798	-253.279099	0.0	0.0
	1.0	1.0	106.051193	30969.785156	11162.735352	2397.134277	89898.031250	833711.500000	-3727165.50	...	56.761074	-273.126740	-24.101181	71.550865	-24.101181	-253.659668	0.0	0.0
	2.0	2.0	106.080574	30969.617188	11162.742188	2397.418457	89898.500000	830116.062500	-3727202.25	...	56.446213	-273.271484	-24.278463	71.196846	-24.278454	-254.108627	0.0	0.0
	3.0	3.0	106.156960	30969.410156	11162.755859	2397.761719	89899.085938	825838.000000	-3727247.25	...	56.070446	-273.440216	-24.481455	70.778709	-24.481472	-254.636780	0.0	0.0
	4.0	4.0	106.315788	30969.125000	11162.772461	2398.181641	89899.789062	820750.562500	-3727302.75	...	55.624382	-273.636627	-24.711657	70.288116	-24.711660	-255.255615	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
394	9994.0	9994.0	478.808197	30542.000000	11551.901367	2586.216797	101716.429688	-131985.062500	-4578958.00	...	-2.618964	-398.338196	-9.657487	15.362790	-9.657487	-397.040253	-0.0	0.0
	9995.0	9995.0	481.056549	30540.406250	11551.897461	2585.313965	101715.742188	-131984.109375	-4578960.50	...	-2.619115	-398.339264	-9.657401	15.362783	-9.657397	-397.040802	-0.0	0.0
	9996.0	9996.0	482.136139	30542.984375	11551.901367	2586.417236	101716.562500	-131981.953125	-4578968.00	...	-2.619285	-398.340363	-9.657289	15.362905	-9.657287	-397.041443	-0.0	0.0
	9997.0	9997.0	486.106934	30540.878906	11551.895508	2585.124268	101715.578125	-131981.218750	-4578969.00	...	-2.619526	-398.341309	-9.657230	15.362894	-9.657231	-397.041718	-0.0	0.0
	9999.0	9999.0	476.031311	30540.619141	11551.898438	2585.870117	101716.164062	-131980.062500	-4578973.50	...	-2.619611	-398.342010	-9.657176	15.362936	-9.657174	-397.041779	-0.0	0.0

	nmolecules	name	earliest	latest	object_count
0	525	NaN	2020-07-21 01:13:13.714240	2020-07-21 01:13:13.714240	1
1	481	NaN	2020-07-21 01:13:13.590214	2020-07-21 01:13:13.590214	1
2	438	NaN	2020-07-21 01:13:13.467257	2020-07-21 01:13:13.467257	1
3	394	NaN	2020-07-21 01:13:13.347795	2020-07-21 01:13:13.347795	1
4	350	NaN	2020-07-21 01:13:13.228101	2020-07-21 01:13:13.228101	1
5	306	NaN	2020-07-21 01:13:13.095156	2020-07-21 01:13:13.095156	1
6	263	NaN	2020-07-21 01:13:12.954722	2020-07-21 01:13:12.954722	1
7	219	NaN	2020-07-21 01:13:12.834089	2020-07-21 01:13:12.834089	1
8	175	NaN	2020-07-21 01:13:12.720613	2020-07-21 01:13:12.720613	1
9	131	NaN	2020-07-21 01:13:12.605763	2020-07-21 01:13:12.605763	1
10	88	NaN	2020-07-21 01:13:12.484941	2020-07-21 01:13:12.484941	1
11	44	NaN	2020-07-21 01:13:12.365566	2020-07-21 01:13:12.365566	1

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	...	Coul-SR:rest-rest	LJ-SR:rest-rest	Coul-14:rest-rest	LJ-14:rest-rest	T-non-Substrate	T-Substrate	Lamb-non-Substrate	Lamb-Substrate
nmolecules	step
525	0.0	0.0	783.015930	40890.703125	15882.089844	3592.712158	135596.328125	-152611.906250	-4587028.00	...	-4133381.00	477655.56250	0.0	0.0	298.593903	0.0	1.0	1.0
	1.0	1.0	6907.512207	70606.929688	19269.679688	4622.277344	134504.187500	-205344.859375	-4144421.25	...	-3685390.75	421481.31250	0.0	0.0	206.871918	0.0	1.0	1.0
	2.0	2.0	7911.669922	76077.703125	19949.109375	5150.765137	134709.843750	-225665.906250	-4045137.25	...	-3581715.00	400387.68750	0.0	0.0	237.620514	0.0	1.0	1.0
	3.0	3.0	8569.228516	79897.421875	20112.382812	5052.904785	134344.406250	-243986.187500	-3969531.50	...	-3503647.75	381790.15625	0.0	0.0	256.506989	0.0	1.0	1.0
	4.0	4.0	9247.992188	81979.546875	20468.628906	5238.154297	134049.062500	-262460.312500	-3907415.75	...	-3439932.50	363504.96875	0.0	0.0	268.449738	0.0	1.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
175	96.0	96.0	3109.290039	27439.615234	6442.884277	1676.433350	44899.921875	-268570.968750	-3638488.25	...	-3468710.25	344307.25000	0.0	0.0	299.187256	0.0	1.0	1.0
	97.0	97.0	2967.511475	27295.527344	6436.663574	1702.368286	44909.246094	-268249.593750	-3639971.00	...	-3470224.50	344559.75000	0.0	0.0	298.442017	0.0	1.0	1.0
	98.0	98.0	3004.910400	27397.394531	6368.225586	1700.576782	44766.316406	-265549.937500	-3640810.25	...	-3471301.50	347079.31250	0.0	0.0	296.464905	0.0	1.0	1.0
	99.0	99.0	3117.757324	27014.894531	6452.667480	1762.188354	44937.621094	-267507.687500	-3640443.50	...	-3470714.00	345275.46875	0.0	0.0	297.777740	0.0	1.0	1.0
	100.0	100.0	2942.428955	27784.935547	6396.532715	1638.451782	44793.921875	-265345.781250	-3641945.25	...	-3472599.00	347461.12500	0.0	0.0	297.373413	0.0	1.0	1.0

	uuid	name	uri	earliest	latest	object_count
0	71a46b71-f815-4c3b-8671-01eefa3efdae	2020-07-21-01-13-13-736370-n-525-m-525-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-736370-n-525-m-525-gromacsnptequilibration	2020-07-21 01:13:13.743269	2020-07-21 01:13:13.743321	21
1	f2919ee2-ba4f-47d9-b1b6-a7d7dd16680c	2020-07-21-01-13-13-610584-n-481-m-481-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-610584-n-481-m-481-gromacsnptequilibration	2020-07-21 01:13:13.616543	2020-07-21 01:13:13.616607	21
2	70b89243-3b84-4ae2-b2a6-c97acc07a8b8	2020-07-21-01-13-13-489638-n-438-m-438-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-489638-n-438-m-438-gromacsnptequilibration	2020-07-21 01:13:13.498771	2020-07-21 01:13:13.498824	21
3	cdea2f6a-d027-419d-85e9-7337a4d8cdb3	2020-07-21-01-13-13-368152-n-394-m-394-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-368152-n-394-m-394-gromacsnptequilibration	2020-07-21 01:13:13.373890	2020-07-21 01:13:13.373943	21
4	53c2d3bb-4db1-47be-9651-2dab3fd76f18	2020-07-21-01-13-13-246509-n-350-m-350-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-246509-n-350-m-350-gromacsnptequilibration	2020-07-21 01:13:13.252270	2020-07-21 01:13:13.252321	21
5	902c7c8b-ad91-4e19-a146-98593a8ca541	2020-07-21-01-13-13-126952-n-306-m-306-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-126952-n-306-m-306-gromacsnptequilibration	2020-07-21 01:13:13.133684	2020-07-21 01:13:13.133737	21
6	0c07311d-ba39-43b0-91f3-395f9acd1d72	2020-07-21-01-13-12-975341-n-263-m-263-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-975341-n-263-m-263-gromacsnptequilibration	2020-07-21 01:13:12.982317	2020-07-21 01:13:12.982371	21
7	2c309f81-c757-478e-a8f1-0a129c9bd1dd	2020-07-21-01-13-12-854269-n-219-m-219-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-854269-n-219-m-219-gromacsnptequilibration	2020-07-21 01:13:12.860518	2020-07-21 01:13:12.860571	21
8	6674851e-222d-42d4-b9d6-9c86332cadd8	2020-07-21-01-13-12-738848-n-175-m-175-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-738848-n-175-m-175-gromacsnptequilibration	2020-07-21 01:13:12.744915	2020-07-21 01:13:12.744970	21
9	102deb24-224e-46bf-9e65-e1735bd3b754	2020-07-21-01-13-12-624372-n-131-m-131-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-624372-n-131-m-131-gromacsnptequilibration	2020-07-21 01:13:12.630556	2020-07-21 01:13:12.630611	21
10	29ddbd49-e5e4-4731-a757-8e43d261a397	2020-07-21-01-13-12-508896-n-88-m-88-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-508896-n-88-m-88-gromacsnptequilibration	2020-07-21 01:13:12.514976	2020-07-21 01:13:12.515029	21
11	ae7b32fd-ef3e-411f-b958-9521cb267731	2020-07-21-01-13-12-384927-n-44-m-44-gromacsnptequilibration	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-384927-n-44-m-44-gromacsnptequilibration	2020-07-21 01:13:12.391877	2020-07-21 01:13:12.391930	21

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	...	Coul-SR:rest-rest	LJ-SR:rest-rest	Coul-14:rest-rest	LJ-14:rest-rest	T-non-Substrate	T-Substrate	Lamb-non-Substrate	Lamb-Substrate
nmolecules	step
306	0.0	0.0	5325.992676	47985.746094	11606.020508	3002.746826	78007.468750	-280553.81250	-3672588.50	...	-3389411.50	338140.34375	0.0	0.0	299.376801	1.706116	1.0	1.0
	1.0	1.0	5064.854492	47531.800781	11415.012695	3003.728760	78207.984375	-318681.56250	-3668624.75	...	-3381978.25	337497.15625	0.0	0.0	297.617889	298.927551	1.0	1.0
	2.0	2.0	5348.880371	47413.351562	11541.583008	3216.615479	78160.101562	-319668.31250	-3678709.75	...	-3393210.25	341217.00000	0.0	0.0	297.264893	178.977844	1.0	1.0
	3.0	3.0	5223.317871	47331.582031	11387.572266	3029.078125	78118.992188	-323640.03125	-3678432.25	...	-3391972.00	339620.65625	0.0	0.0	298.663605	122.084465	1.0	1.0
	4.0	4.0	5442.667969	47610.261719	11536.816406	3017.707031	78092.875000	-323507.34375	-3680309.50	...	-3394937.75	342139.40625	0.0	0.0	297.389465	88.929482	1.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
44	96.0	96.0	686.234436	6744.473145	1549.529541	380.422516	11241.458008	-299507.09375	-3592958.25	...	-3545980.25	356860.31250	0.0	0.0	296.285767	43.927448	1.0	1.0
	97.0	97.0	718.663269	6564.708008	1520.251953	434.862701	11267.512695	-298329.12500	-3596315.75	...	-3549566.50	357762.53125	0.0	0.0	297.457886	42.531563	1.0	1.0
	98.0	98.0	747.969116	6651.683594	1554.880127	392.532532	11219.794922	-298274.78125	-3595212.00	...	-3547983.50	357772.53125	0.0	0.0	295.978271	43.907619	1.0	1.0
	99.0	99.0	674.302551	6480.134277	1570.608276	429.110443	11294.245117	-300619.25000	-3590330.50	...	-3543363.50	355433.78125	0.0	0.0	296.465057	43.936378	1.0	1.0
	100.0	100.0	712.136780	6444.956055	1589.672607	443.459991	11324.289062	-297933.50000	-3595668.75	...	-3548920.50	358164.68750	0.0	0.0	296.528442	42.908562	1.0	1.0

	uuid	name	uri	earliest	latest	object_count
0	a0822e65-a0e5-45f5-8744-3227b09ce877	2020-07-21-01-13-13-750539-n-525-m-525-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-750539-n-525-m-525-gromacsrelaxation	2020-07-21 01:13:13.756693	2020-07-21 01:13:13.756743	21
1	268dea24-811a-4159-b47b-40f6ab74f0e6	2020-07-21-01-13-13-623594-n-481-m-481-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-623594-n-481-m-481-gromacsrelaxation	2020-07-21 01:13:13.629738	2020-07-21 01:13:13.629793	21
2	960fe805-a615-4600-82ff-a99977fddc78	2020-07-21-01-13-13-504608-n-438-m-438-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-504608-n-438-m-438-gromacsrelaxation	2020-07-21 01:13:13.510923	2020-07-21 01:13:13.511002	21
3	053386b4-1631-4714-9ca5-f335a1081c3d	2020-07-21-01-13-13-381288-n-394-m-394-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-381288-n-394-m-394-gromacsrelaxation	2020-07-21 01:13:13.387565	2020-07-21 01:13:13.387620	21
4	cefa70fb-6767-459e-9edb-5e49ef7b340e	2020-07-21-01-13-13-259185-n-350-m-350-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-259185-n-350-m-350-gromacsrelaxation	2020-07-21 01:13:13.264831	2020-07-21 01:13:13.264881	21
5	b3f6c4fd-55ec-43c0-914a-4cc0dfbe6309	2020-07-21-01-13-13-140608-n-306-m-306-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-13-140608-n-306-m-306-gromacsrelaxation	2020-07-21 01:13:13.147043	2020-07-21 01:13:13.147095	21
6	41b27d95-1e58-48c4-8ba9-35f5a6b2ef25	2020-07-21-01-13-12-990048-n-263-m-263-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-990048-n-263-m-263-gromacsrelaxation	2020-07-21 01:13:12.997202	2020-07-21 01:13:12.997258	21
7	ece461b3-466e-4eca-a028-4bce74a4ff8c	2020-07-21-01-13-12-867252-n-219-m-219-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-867252-n-219-m-219-gromacsrelaxation	2020-07-21 01:13:12.873911	2020-07-21 01:13:12.873966	21
8	fdcb613e-5a26-4cd4-8a2d-c91e624f25ae	2020-07-21-01-13-12-751002-n-175-m-175-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-751002-n-175-m-175-gromacsrelaxation	2020-07-21 01:13:12.757246	2020-07-21 01:13:12.757301	21
9	47f93833-95b6-482f-aa8a-b0f41155382a	2020-07-21-01-13-12-637269-n-131-m-131-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-637269-n-131-m-131-gromacsrelaxation	2020-07-21 01:13:12.643612	2020-07-21 01:13:12.643665	21
10	9269d5d5-93a4-4988-b7e4-8802e273728f	2020-07-21-01-13-12-521063-n-88-m-88-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-521063-n-88-m-88-gromacsrelaxation	2020-07-21 01:13:12.527302	2020-07-21 01:13:12.527356	21
11	05ae33bc-5585-49eb-990d-0e45a52e0a8c	2020-07-21-01-13-12-397569-n-44-m-44-gromacsrelaxation	file://juwels01.ib.juwels.fzj.de/p/project/chfr13/hoermann4/dtool/DATASETS/2020-07-21-01-13-12-397569-n-44-m-44-gromacsrelaxation	2020-07-21 01:13:12.403514	2020-07-21 01:13:12.403569	21

		Time	Bond	U-B	Proper Dih.	LJ-14	Coulomb-14	LJ (SR)	Coulomb (SR)	...	Coul-SR:rest-rest	LJ-SR:rest-rest	Coul-14:rest-rest	LJ-14:rest-rest	T-non-Substrate	T-Substrate	Lamb-non-Substrate	Lamb-Substrate
nmolecules	step
44	0.0	0.0	727.516357	6460.637695	1589.852173	442.834778	11321.890625	-297654.65625	-3595528.25	...	-3548792.50	358401.62500	0.0	0.0	297.732666	42.913223	1.0	1.0
	1.0	1.0	637.103760	6680.111816	1664.305664	436.721252	11223.439453	-299945.03125	-3591431.00	...	-3544092.00	356309.84375	0.0	0.0	296.206055	43.141201	1.0	1.0
	2.0	2.0	642.045593	6645.769043	1640.131836	433.048035	11230.026367	-293239.03125	-3597814.50	...	-3551162.75	363227.90625	0.0	0.0	296.402557	43.175102	1.0	1.0
	3.0	3.0	689.675598	6495.912109	1608.256226	447.823822	11255.919922	-296287.34375	-3595840.25	...	-3548502.50	359893.12500	0.0	0.0	296.442810	44.660847	1.0	1.0
	4.0	4.0	690.900574	6662.355469	1624.773682	440.674133	11202.725586	-298627.62500	-3593248.75	...	-3545816.75	357717.09375	0.0	0.0	296.806183	44.131638	1.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
88	196.0	196.0	1407.950073	13195.257812	3294.906006	900.900635	21675.939453	-305988.68750	-3603197.25	...	-3500001.75	352925.18750	0.0	0.0	296.846649	43.653877	1.0	1.0
	197.0	197.0	1408.531982	13111.499023	3227.258789	865.946228	21715.794922	-304492.09375	-3605858.75	...	-3502690.25	354329.21875	0.0	0.0	297.602112	44.336353	1.0	1.0
	198.0	198.0	1408.231567	13204.254883	3365.327881	855.522583	21777.230469	-304386.93750	-3605296.50	...	-3501683.25	354172.06250	0.0	0.0	297.060028	43.724331	1.0	1.0
	199.0	199.0	1368.782837	13244.612305	3242.871582	830.326782	21635.414062	-303648.37500	-3604465.00	...	-3500951.50	355047.65625	0.0	0.0	296.349640	45.628654	1.0	1.0
	200.0	200.0	1432.058594	13058.166992	3266.754395	802.775940	21658.347656	-305025.87500	-3602718.25	...	-3498495.50	353472.28125	0.0	0.0	296.858856	43.783363	1.0	1.0